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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Obtaining Public Opinion about sUAS Activity in an Urban Environment

The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research project has been developing and testing concept ideas for enabling small UAS (sUAS) operations in low altitude airspace (ground to 400 feet). To do this, a series of flight test demonstrations were organized over five years at seven test sites. Technology Capability Level-4 (TCL4), the most complex flight tests, were conducted in Texas, USA, during August 2019. This testing resulted in over 400 data collection flights using eight live rotorcraft, with nine flight crews flying pre-planned scenarios in the urban downtown and waterfront areas of Corpus Christi, Texas. Test scenarios were designed to include a variety of elements, including live and simulated vehicles, and personnel in many different roles, including flight crews and mission personnel. One group of people who did not have a role in the flight tests but will be affected by UAS operations as they become more ubiquitous, are the general public. What do the public think about sUAS operations in urban areas? In order to obtain a reference point that noted the public's view of the operations in the TCL4 demonstration, a short survey was developed and offered to members of the public who wished to comment on the sUAS activities they saw in their city during two weeks in August, 2019.Forty five people completed the online public opinion survey. Participants volunteered to take it, creating a self-selected sample. The survey included eleven questions that asked participants about their level of comfort and concerns with UAS activity; their knowledge of UAS operations; and, whether a traffic management system like UTM would increase their confidence in urban UAS activity. The general public in Corpus Christi showed a good level of knowledge of sUAS regulations, with almost half of their responses to the knowledge portion of the survey being correct. They expressed a moderate level of concern (x = 4.7 out of 7) at urban sUAS activity and, of those who cited a concern, the majority reported this was about privacy (54%), which are all responses in line with those reported in earlier research on public opinion. Views on UTM were mixed, with respondents indicating they thought the introduction of UTM will improve safety somewhat (5.2 out of 7) but it will also increase their concern a little (4.6 out of 7). This would indicate there needs to be more information and educational material about the UTM concept made available to the public with the aim to reduce public concerns about the system.

Caterina Grossi↗
NASA NTRSDate not supplied

Obtaining Public Opinion About sUAS Activity in an Urban Environment

The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research project has been developing and testing concept ideas for enabling small UAS (sUAS) operations in low altitude airspace (ground to 400 feet). To do this, a series of flight test demonstrations were organized over five years at seven test sites. Technology Capability Level-4 (TCL4), the most complex flight tests, were conducted in Texas, USA, during August 2019. This testing resulted in over 400 data collection flights using eight live rotorcraft, with nine flight crews flying pre-planned scenarios in the urban downtown and waterfront areas of Corpus Christi, Texas. Test scenarios were designed to include a variety of elements, including live and simulated vehicles, and personnel in many different roles, including flight crews and mission personnel. One group of people who did not have a role in the flight tests but will be affected by UAS operations as they become more ubiquitous, are the general public. What do the public think about sUAS operations in urban areas? In order to obtain a reference point that noted the public’s view of the operations in the TCL4 demonstration, a short survey was developed and offered to members of the public who wished to comment on the sUAS activities they saw in their city during two weeks in August, 2019. Forty five people completed the online public opinion survey. Participants volunteered to take it, creating a self-selected sample. The survey included eleven questions that asked participants about their level of comfort and concerns with UAS activity; their knowledge of UAS operations; and, whether a traffic management system like UTM would increase their confidence in urban UAS activity. The general public in Corpus Christi showed a good level of knowledge of sUAS regulations, with almost half of their responses to the knowledge portion of the survey being correct. They expressed a moderate level of concern (x = 4.7 out of 7) at urban sUAS activity and, of those who cited a concern, the majority reported this was about privacy (54%), which are all responses in line with those reported in earlier research on public opinion. Views on UTM were mixed, with respondents indicating they thought the introduction of UTM will improve safety somewhat (5.2 out of 7) but it will also increase their concern a little (4.6 out of 7). This would indicate there needs to be more information and educational material about the UTM concept made available to the public with the aim to reduce public concerns about the system.

UTM↗
NASA NTRSDate not supplied

Establishing an Urban Heat Exposure Severity Index for Infrastructure Prioritization in Tempe, Arizona, Using NASA Earth Observations and LiDAR

Located on the banks of the Salt River in the Sonoran Desert, Tempe, Arizona, features a semi-arid climate with summer daily maximum temperatures regularly exceeding 37.8°C. Tempe is also subject to the southwestern monsoon season from July-September and the humidity exacerbates the high temperatures. Furthermore, the rapid urbanization experienced in Tempe has resulted in an intensification of the urban heat island. The summer of 2020 shattered the previous record of days exceeding 43.4°C, leading to higher energy and water costs, lower comfort, and increased risk of heat stroke for residents. Recognizing the impacts of extreme heat, the City of Tempe partnered with the Healthy Urban Environments initiative and NASA DEVELOP to identify census tracts that experience a higher mean land surface temperature than the city average. The NASA DEVELOP team used remotely sensed land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and albedo data calculated from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments from 2015 to 2020 to create heat hazard and exposure maps. LiDAR point cloud data, provided by the United States Geological Survey through Arizona State University’s Map and Geospatial Hub, were used to derive 3D buildings, building footprints, and tree point data for a shading analysis of walking paths, roads, and buildings at the census tract level. In situ meteorological measurements including air temperature and humidity were used to compare the macro-scale temperature measurements. The team worked with the City of Tempe to develop a methodology to process available data and identify areas of highest concern for urban heat effects within the city. With these insights, Tempe, Arizona can better address these issues with data-driven information to make decisions regarding heat mitigation and adaptation efforts.

John Dialesandro↗
NASA NTRS2021

Small UAV Flight Planning in Urban Environments

This work proposes a fast algorithm for generating obstacle-free and wind-efficient flight paths at a constant above-ground-level altitude in urban environments because a fast flight path planning algorithm is an essential function or service needed for enabling small unmanned aerial vehicle (sUAV) to operate in urban environments within Class G airspace. The proposed method first converts the 3D path planning problem to a 2D problem by constructing an obstacle map at a given above-ground-level altitude. A quad-tree decomposition is then used to build a search space in terms of obstacle occupancy and wind difference. The wind cost of traveling through each cell is defined based on energy consumption under various wind conditions. A repulsive potential is also adopted to make sure that the flight plans stay away from obstacles. The Theta* search algorithm, a variant of A* algorithm, is applied to mitigate the path angle change constraints introduced by grid-based graphs. With the Theta* and postsmoothing techniques, an obstacle-free, wind efficient, and constant above-ground-level flight plan can be quickly generated for sUAV operations in urban environments while meeting the lateral path angle constraints. The results showed that the path planning algorithm is efficient and can be finished within several seconds. With a proper choice of wind coefficient, the proposed path planning algorithm outperforms the multiple-shooting trajectory optimization method even in an obstacle-free environment. With the flexibility of incorporating other geo-related costs and computation efficiency, the proposed algorithm shows the potential for real-time flight path planning in complex urban environments.

Path planning↗
NASA NTRSDate not supplied

Assessing Environmental and Socioeconomic Factors of Urban Flood Vulnerability in Kansas City, Kansas

Pluvial flooding, over-saturated ground, and drainage systems disproportionately impact historically marginalized urban neighborhoods during extreme rainfall events. These communities are impacted by physical and socioeconomic factors that make them vulnerable to flooding events, such as high concentrations of impervious landcover, high precipitation rates, and a combined sewer system framework. Despite known vulnerability to environmental hazards, understanding potential pluvial street-level flooding events are largely unknown. Using the open-source National Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation model, NASA DEVELOP examined neighborhood scale runoff retention and potential economic damages for risk mapping throughout Kansas City, Kansas. The generated outputs aid in identifying vulnerable neighborhoods susceptible to flooding and in need of future intervention. Previous studies have applied this model framework to understand urban flood vulnerability regarding ecosystem services, urban planning, and flood mitigation strategies. We utilized the InVEST outputs to develop indices pertaining to environmental justice factors of race, socioeconomic status, social vulnerability, and health. The findings indicate that historically redlined neighborhoods in Kansas City, Kansas face disproportional impacts from flood events and are subject to greater environmental stressors. This research provides an approach to utilizing an open-source flood vulnerability model to empower neighborhood-scale environmental justice analysis, enhancing the local communities' understanding of present-day impacts of historical environmental injustices.

Hadwynne Gross↗
NASA NTRSDate not supplied

Cyber Resiliency and the Implementation of a Host-Based Intrusion Detection System in an Urban Air Mobility Environment

With the growth in Urban Air Mobility systems and the increasing reliance on interconnected technologies, ensuring the security of these complex components has become critical. As cities evolve into smart urban centers, the vulnerability to cyber threats escalates, possibly endangering citizens safety and the efficiency of transportation networks.In response to these challenges, this paper presents a study on the need for cyber resilient techniques within future air traffic environments. It will pay specific attention to the implementation of a Host-Based Intrusion Detection System (HIDS) utilizing Atomic OSSEC software, tailored specifically to a NASA simulation of an UrbanAirMobility environments’ unique demands. Further, this study seeks to outline the rational for NASA’s recommendation for a HIDS in such environments. It explores the design, development, and deployment of the proposed HIDS, focusing on its adaptability to monitor the hybrid nature of the Urban Air Mobility environment. Leveraging machine learning algorithms and anomaly detection techniques, the HIDS is equipped to continuously monitor and analyze the behavior of individual host systems, vehicles, and devices, thereby providing a proactive approach to threat detection. Implementing a HIDS is a pivotal strategy for enhancing cyber resiliency, as it gives an organization granular visibility into internal system activities, enables rapid detection and response to anomalous behavior and cyber threats, and fortifies the organizations overall cybersecurity posture. Finally, this study aims to provide recommendations and include learned takeaways that the Urban Air Mobility industry should consider. In brief, this paper highlights the significance of host-based intrusion detection in UrbanAirMobility environments and underscores the necessity of tailored security solutions to safeguard against emerging cyber threats.

UAM↗
NASA NTRSDate not supplied

Enabling Smart Urban Airspaces through Distributed Sensing Technologies

Distributed sensing systems offer potential for enhancing safety and efficiency in urban airspaces and low-altitude metropolitan flight corridors. These systems leverage sensor networks to provide real-time airspace monitoring, situational awareness, and data for decision-making. This paper examines application of distributed sensing and smart airspace concepts to enable advanced urban flight, focusing on critical need applications such as airspace monitoring, corridor surveillance, precision navigation, and hazard avoidance. Benefits such as improved monitoring and increased situational awareness of activity within the urban airspace are considered alongside challenges in system design and implementation. This paper summarizes an ongoing effort to study the feasibility and effectiveness of distributed sensing systems for enabling urban airspace operations. Current research and design considerations are summarized, and a proposed roadmap for future work is presented.

Corey A Ippolito↗
NASA NTRS2024

Nonuniform Response of Vegetation Phenology to Daytime and Nighttime Warming in Urban Areas

The phenological response of vegetation to changing climates requires exploration across environments, including urban areas with elevated daytime and nighttime temperatures (T D and T N ) and artificial lights. Here, we analyzed multiple remotely-sensed datasets (2012–2021) from and around a highly-urbanized area, Seoul, in South Korea. While the urban fraction and artificial lights largely contributed to advancing the start-of-season (SOS) and delaying the end-of-season (EOS) (30.7–42.1%), SOS changes were more driven by T D (15.7%), whereas EOS changes were more attributed to T N (21.8%). A higher response of the SOS to T D was found in more urbanized areas with higher artificial lights, while the sensitivity of the EOS to T N increased in brighter areas during nighttime. These findings indicate that vegetation’s response to rising temperatures will become dynamic, given the potential constraints by light and other attributes, calling for further assessments of its implication for terrestrial ecosystems and carbon cycle.

JiHyun Kim↗
DOE OSTI2020

Urban-scale Energy Modeling: Scaling Beyond Tax Assessor Data

In an attempt to attain building-specific characteristics for urban-scale building energy models, county-specific tax assessors’ data is often an initial data source. This data source can contain valuable information such as year built, area, height, HVAC type, and roof/wall descriptions.We will show examples of 2,000 fields from Hamilton County in Tennessee with examples of many fields which are not relevant to urban-scale building energy modeling, are incorrect compared to other data sources, and highlight some lessons learned working with such a data source.There are currently 3,142 counties in the United States, each with their own data format, field definitions, and data access policy. As urban-scale involves city-scale analysis potentially covering multiple counties and matures toward state- or nation-scale analysis, county-by-county approaches are not scalable. While there are efforts to unify these datasets, there is an increasing proliferation of data and algorithms that can cover wider areas and provide more accurate inputs for urban-scale models. This paper summarizes computer vision of imagery, cartographic layers, building type assessment, and model generation used to achieve scalable detection and analysis of buildings.

New, Joshua↗
DOE OSTI2021

Urban Energy Systems: Research at Oak Ridge National Laboratory

In the coming decades, our planet will witness unprecedented urban population growth in both established and emerging communities. The development and maintenance of urban infrastructures are highly energy-intensive. Urban areas are dictated by complex intersections among physical, engineered, and human dimensions that have significant implications for traffic congestion, emissions, and energy usage. In this chapter, we highlight recent research and development efforts at Oak Ridge National Laboratory (ORNL), the largest multipurpose science laboratory within the U.S. Department of Energy’s (DOE) national laboratory system, that characterizes the interactions between the human dynamics and critical infrastructures in conjunction with the integration of four distinct components: data, critical infrastructure models, and scalable computation and visualization, all within the context of physical and social systems. Discussions focus on four key topical themes: population and land use, sustainable mobility, the energy-water nexus, and urban resiliency, that are mutually aligned with DOE’s mission and ORNL’s signature science and technology capabilities. Using scalable computing, data visualization, and unique datasets from a variety of sources, the institute fosters innovative interdisciplinary research that integrates ORNL expertise in critical infrastructures including energy, water, transportation, and cyber, and their interactions with the human population.

Bhaduri, Budhu↗
DOE OSTI2021

A Modularized Urban Scale Building Energy Modeling Framework Designed with An Open Mind

In recent years, physics-based building energy modeling (BEM) has started being used to evaluate the performance of buildings in the context of connected communities and on an urban scale to study their aggregated energy use, interactions, and impacts on the energy supply infrastructure and environment. The development of urban-scale BEM solutions needs extensive effort. Existing attempts tend to focus on different aspects of BEM on an urban scale, such as collecting as-built building data from different information sources, integrating geometry modeling with geographic information systems (GISs), representing operational and occupancy profiles, automating workflow, processing and visualizing the results, and conducting large-scale simulations. Urban-scale BEM development would benefit from multi-disciplinary research areas and from an open platform to adopt advancements on data sources and tools. For these purposes, this research proposes a modularized bottom-up model creation and simulation framework that is built on the state-of-the-art BEM tools and can accommodate different building stock data. This framework uses a standardized schema to describe building design and operational characteristics, and it can be instantiated from different building survey datasets with heterogeneous structures. The paper demonstrates how thousands of surveyed buildings from the 2012 U.S. Energy Information Administration’s Commercial Buildings Energy Consumption Survey (CBECS) were one-to-one converted to EnergyPlus models through the schema and the model generation process, then simulated with distributed computing, and their results are summarized.

Lei, Xuechen↗
DOE OSTI2025

Stream Temperature Responses to Summer Urban Rain Events Along the Savannah River

The hydrological urban heat island (HUHI) is a recent facet of the urban heat island (UHI), describing thermal enhancement of bodies of water in response to urbanization. Although the forefront of this work has been studied for large Metropolitan areas, this effect on developing cities is currently unknown. To locally measure and quantify HUHI effects in a developing city, we utilized Multi-Radar Multi Sensor (MRMS) radar-derived rainfall data to estimate rainfall in the Augusta Metropolitan Area (AMA) and the Savannah Metropolitan Area (SMA), two developing communities adjacent to the Savannah River. We analyzed mean temperature trends of two stream gauges measuring temperature at, and downstream of the central AMA and the SMA. Results show overall peak stream temperatures within AMA are reached within a 2hr timeframe with surge temperatures between 1-2 K, perhaps assisted by the presence of the Augusta Shoals further upstream. The SMA appears to have little HUHI effects due to urban greening and impacts to coastal meteorology. It is suggested that Moderate rainfall events (10-25mm of rainfall) have a significant correlation (~0.1 correlation coefficient with a significance level of 0.05) between stream temperature and rainfall, indicating potential warmer runoff input into the Savannah River. Although results seem promising, further research into moderate rainfall events are needed to determine the extent of the HUHI for AMA.

Wermter, Joseph E. [Savannah River National Labora↗
NASA NTRS2021

Examining Runner’s Outdoor Heat Exposure Using Urban Microclimate Modeling and GPS Trajectory Mining

It is important to quantify human heat exposure in order to evaluate and mitigate the negative impacts of heat on human well-being in the context of global warming. This study proposed a human-centric framework to examine human personal heat exposure based on anonymous GPS trajectories data mining and urban microclimate modeling. The mean radiant temperature (Tmrt) that represents the human body’s energy balance was used to indicate human heat exposure. The meteorological data and high-resolution 3D urban model generated from multispectral remotely sensed images and LiDAR data were used as inputs in urban microclimate modeling to map the spatio-temporal distribution of the Tmrt in the Boston metropolitan area. The anonymous human GPS trajectory data collected from fitness Apps was used to map the spatiotemporal distribution of human outdoor activities. By overlaying the anonymous GPS trajectories on the generated spatio-temporal maps of Tmrt, this study further examined the heat exposure of runners in different age-gender groups in the Boston area. Results show that there is no significant difference in terms of heat exposure for female and male runners. The female runners in the age of 45-54 are exposed to more heat than female runners of 18-24 and 25-34, while there is no significant difference among male runners. This study proposed a novel method to estimate human heat exposure, which would shed new light on mitigating the negative impacts of heat on human health.

Personal heat exposure↗
NASA NTRS2026

Using Satellite Surface Temperature Data to Monitor Urban Heat Island

Exposure to heat is a growing concern nationwide. Temperatures can be elevated in cities compared to surrounding rural areas, referred to as an “urban heat island” (UHI) effect, which is intensified during heat waves. The lack of dense networks of air temperature measurements results in few studies on urban heat. Now, a vast amount of high spatial and temporal satellite data on land surface temperature is available. We identified satellite datasets with the longest surface temperature records but different spatial and temporal resolutions: Landsat (1985-current, biweekly at 60 m and 100 m spatial resolution) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites (2000-current, daily at 1 km spatial resolution). We investigated how satellites with different spatial and temporal resolutions detect UHI effects differently. We hypothesized that 1) a dataset’s spatial resolution impacts the precision of detected UHIs spatially, since high spatial resolution Landsat data better captures spatial variability in temperature, and 2) daily surface temperature data can detect temporal patterns of UHIs and heatwave frequencies. We analyzed Landsat and MODIS satellite data in the Washington, D.C. and Baltimore region. We found that Landsat describes higher spatial variability of the UHI effect than MODIS data. However, MODIS data shows more consistent seasonal surface temperature patterns than Landsat when compared to in situ air temperature measurements. MODIS data was also able to consistently measure the frequency of heat waves. This study demonstrates the value of NASA satellite data for urban heat and climate change studies.

landsat↗
DOE OSTI2023

Upcycling and urban mining for nanomaterial synthesis

Responsible processing of complex waste streams, especially through resource recovery and upgrading, is essential to lessen the environmental and societal impacts of materials production and disposal. Upcycling waste into materials that have higher value than their original form, and urban mining, the recovery of metals from electronic waste, are two recent strategies in waste management. Sustainable cradle-to-grave manufacturing means recovering resources that would otherwise contaminate the planet. Here, we discuss upcycling of plastic, rubber, and related carbon waste streams into graphene, as well as the recovery of precious metals and rare earth elements, while removing toxic heavy metals, all using flash Joule heating. Furthermore, this article provides a brief overview of the current state of upcycling and urban mining as it applies to materials conversion, recovery, or nanomaterial synthesis, with a concluding perspective highlighting the considerations, opportunities, and challenges that accompany upcycling and urban mining research.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Residential segregation and outdoor urban moist heat stress disparities in the United States

The combined impact of urbanization-induced warming and drying on large-scale heat stress disparities remains unknown, with multicity studies using satellite-derived land surface temperature as a proxy for these disparities. Here, using high-resolution urban-resolving numerical model simulations for 2014–2018, we find pervasive disparities in all-sky average maximum summertime air temperature and moist heat stress metrics across US cities, with higher outdoor heat stress exposure in poorer and primarily non-white census tracts. Ninety-four percent of the US urban population (228 million) live in cities where heat stress burdens the poor, with heat stress inequities between white and non-white populations strongly associated with residential segregation. Similarly, historically redlined neighborhoods show higher heat stress than their non-redlined counterparts, demonstrating how historical segregation relates to present-day environmental inequalities. Our results provide quantitative estimates of physiologically relevant heat stress disparities at the US national scale and highlight potential biases when using satellites as a proxy for these.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2023

Stakeholder analysis for designing an urban air quality data governance ecosystem in smart cities

Cities, the world over, are fuelling economic growth. At the same time, rapid urbanization is a root cause of serious environmental damage. Recent WHO global air pollution guidelines highlight air pollution as a critical environmental threat along with climate change. To address these threats, smart cities and clean air programs are on a rise. In smart cities, data and Information and Communication Technologies (ICT) are major drivers of city transformations. The 4th Industrial Revolution (4IR) technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing have the potential to accelerate these transformations toward urban resilience. However, the success of smart cities and clean air programs depends on cohesive multi-sector stakeholder contributions. This study conducted interdisciplinary participative stakeholder analysis to understand the data, and sectorial challenges, to outline the technological opportunities to facilitate clean air programs in Indian smart cities. The research highlights gaps due to siloed stakeholder operations, lack of data calibration, non-alignment of smart city and air quality management services, non-availability of health exposure data, and difficulty in translating scientific data into implementable actions. Stakeholders expressed potential ‘fit for the purpose’ use of IoT devices, satellites, smartphones, and mobility data augmented by AI methods in bridging these gaps. In conclusion, the analysis points toward a need to develop an easily accessible and ubiquitous urban data governance ecosystem enabling seamless cross-sector data exchanges to build trusting relationships among the stakeholders across the air quality management value chain.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗