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Smart and connected communities use technology to better manage their urban energy systems and improve the quality and performance of government services by leveraging big data for data-driven decisions. NREL helps these communities reach their clean energy goals through cutting-edge expertise in planning, data, analytical tools, and technical support.
The Amazon forest is being converted to urban and agricultural uses through land clearing including large scale burning. It is also the dominant source of biogenic hydrocarbons globally, which can chemically transform in the atmosphere to form secondary organic aerosol (SOA). Most aerosols (solid or liquid particles suspended in air) are organic and formed through secondary chemical processes. This means that SOA chemical composition and their physical properties can impact cloud formation, the hydrologic cycle, and radiative balance in this region. The 2014 GoAmazon field campaign at the DOE/ARM facility at T3 afforded study of chemical transformations in the region downwind of Manaus. Local biogenic hydrocarbons emissions are high, and their chemical oxidation can be studied with varying degrees of influence by the urban plume. We collected aerosol filter samples and made time-resolved molecular level measurements by deploying a sequential filter sampler and a Semi-Volatile Thermal desorption Aerosol Gas Chromatograph (SV-TAG) during Jan-Mar 2014 (wet season) and Aug-Oct 2014 (dry season).
Chromium (Cr) is a redox-sensitive element in contaminated coastal urban soils. Sea level rise (SLR) with subsequent soil inundation may facilitate Cr transformation and mobilization through alterations in local redox conditions and porewater ion composition. Here, we investigated the impact of water salinity and redox conditions on Cr chemistry in these environments. Synchrotron-based X-ray spectroscopy and wet chemical analyses revealed that the soils contained very high levels of Cr (up to 4320 mg kg -1 ) and that chromite (~52%) and Fe-Cr hydroxide coprecipitates (~44%) were the predominant Cr species. The abundance of these two components resulted in low Cr mobility under non-flooded conditions. Chromium(II) was identified in the soils, potentially derived from the waste parent material. Seawater and anoxic conditions resulted in lower Cr release compared to freshwater and aerobic conditions. Up to three to eight times more Cr was released under aerobic conditions versus anaerobic conditions in the freshwater versus saltwater, respectively, with total dissolved Cr values remaining below 0.02 mg L -1 . The decrease in Cr release was likely due to Cr reduction by Fe(II) and sulfide. This work provides important information on how salinity and redox fluctuations impact Cr cycling which is likely to occur during SLR.
This Vision ConOps is intended as a foundation to engage members of the UAM community and provide a consensus on the future vision of UAM operations. It provides a concept for more detailed discussion and a basis for the exploration of ideas using a common framework to inform the continued development and integration of UAM as part of the broader transportation system. Advanced Air Mobility (AAM) encompasses a range of innovative aviation technologies (small drones, electric aircraft, automated air traffic management, etc.) that are transforming aviation’s role in everyday life, including the movement of goods and people. Urban Air Mobility (UAM) represents one of the most exciting and complex AAM concepts with highly automated aircraft, providing commercial services to the public over densely populated cities. This concept has generated tremendous interest and industry investment. UAM envisages a future in which advanced technologies and new operational procedures enable practical, cost-effective air travel as an integral mode of transportation in metropolitan areas. It represents one of the most exciting and complex AAM concepts with highly automated aircraft providing commercial services to the public over densely populated cities. For this reason, the National Aeronautics and Space Administration (NASA) selected UAM as the initial goal of its AAM efforts and the focus of this Vision Concept of Operations (ConOps) document. UAM Community Vision ConOps: This Vision ConOps effort was led by experts from NASA’s Aeronautics Research Mission Directorate (ARMD) in collaboration with the Federal Aviation Administration (FAA) and Deloitte’s Ecosystem Advisory Group (a cohort of advisers with aviation, aerospace, and regulatory expertise). To develop this Vision ConOps, NASA, FAA, and Deloitte built upon the current body of aeronautical research and consulted with more than 100 stakeholder organizations. This UAM community includes entities ranging from legacy aviation leaders to innovators and new market entrants. Stakeholders consulted included the federal government, state and local government, aerospace original equipment manufacturers (OEMs), local transportation organizations, prospective UAM operators, academia, industry standards-setting bodies, airports, service suppliers, and others (as described in Appendix G). This input was captured through the following methods: • A series of more than two dozen interviews with industry experts, federal regulators, state and local governments, and industry trade groups provided insight into the challenges of UAM integration into the National Airspace System (NAS), as well as technology developments and a variety of perspectives as to how UAM systems will integrate. • A series of two-day community workshops enabling active, detailed engagement of nearly 100 industry, academic, federal, and state stakeholder individuals. These workshops, hosted by NASA and Deloitte, explored UAM concepts in detail, and stakeholders were invited to collaboratively analyze and propose solutions to some of the greatest conceptual challenges behind UAM at an intermediate state. • A review of more than 160 sources of UAM literature from across government, industry, and academia, which are listed in Appendix H. • The public sharing of workshop input and document drafts for review and input across the UAM community. Feedback in the form of more than 1,000 comments and inputs on the document was received from industry groups, individual companies, academia, and government (federal, state, and local), among others. Although effort was made to incorporate inputs from across the UAM stakeholder group, not all comments could ultimately be incorporated in this version. The team resolved conflicting comments or ideas while maintaining consistency with the known direction of regulators and ensuring the document was coherent and consistent. It is recognized that this is a rapidly evolving area and that concepts will likely change over time; as such, this Vision ConOps is a living document and is expected to evolve as concepts mature. The ConOps does, however, provide a vision of UAM concepts and solutions based on the broad insights from across the UAM stakeholder community at the time of its publication and is intended to serve as a UAM North Star for continued research and development of UAM. As a broad Vision ConOps, is not a detailed engineering document; rather, it focuses primarily on outlining a broad, high-level vision across all aspects of a UAM transportation system.
The information content of Landsat TM and MSS data was examined to assess the ability to digitally differentiate urban and near-urban land covers around Miami, FL. This examination included comparisons of unsupervised signature extractions for various cover types, training site statistics for intraclass and interclass separability, and band and band combination selection from an 11-band multisensor data set. The principal analytical tool used in this study was transformed divergence calculations. The TM digital data are typically more useful than the MSS data in the homogeneous near-urban land-covers and less useful in the heterogeneous urban areas.
Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.
Advanced Air Mobility is the safe, sustainable, affordable, and accessible aviation for transformational local and intraregional missions.
The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.
Polybrominated diphenyl ethers (PBDEs) are an important class of flame retardants. Annual global demand for these compounds was over 67,000 metric tons in 2001. PBDEs have recently been extensively investigated as environmental contaminants because they have been detected in air, sediment, and tissue samples from urban and remote areas. Important issues include quantifying PBDE partitioning in various environmental compartments, and elucidating transformation pathways. The partitioning of PBDE congeners to aerosols was estimated for 16 sites in the United States, Canada, and Mexico. The aerosol particles were PM2.5, the total suspended particle (TSP) concentration varied between 3.0 - 55.4 micro g/cubic meter, and the organic fraction ranged from 11 - 41%; these data are published values for each site. It is estimated that the largest fraction of each PBDE associated with the aerosol particles occurs in Mexico City, and the smallest fraction in Colorado Plateau. Although the organic fraction in Mexico City is about 60% of that observed in the Colorado Plateau, the TSP is larger by a factor of about 18.5, and it is the difference in TSP that strongly influences the fraction of particle-bound PBDE in this case. PBDE partitioning to PM2.5 particles also varies seasonally because of temperature variations. For the less brominated congeners the percentage that is particle-bound is relatively low, regardless of air temperature. In contrast, the heavier congeners exhibit a significant temperature dependence: as the temperature decreases (fall, winter) the percentage of PBDE that is particle-bound increases. The partitioning calculations complement experimental data indicating that decabromodiphenyl ether (DBDE) dissolved in hexane transforms very rapidly when irradiated with solar light. DBDE is the most highly brominated PBDE congener (10 bromine atoms) and occurs in the commercial formulation which is subject to the largest global demand.
Planetary boundary layer height (PBLH) is often used to characterize the structure of the lower atmosphere. Aerosol lidar, a ground-based remote sensing method, provides the vertical distribution of aerosol at a high temporal resolution observation data, from which, the PBL structure and the position of the PBL top can be comprehensively studied. PBLH determination with lidar data depends primarily on the characteristic turbulent motions in the atmosphere and the geophysical location. However, lidar determination of PBLH over densely populated subtropical locations has rarely been discussed; thus, developing retrieval techniques suitable to these areas is necessary. In this study, four PBLH determination methods (Gradient, δ–threshold, Haar wavelet transform, and hybrid image processing) are applied to estimate the PBLH from lidar observations over an urban area in East Asia, and one—the Gradient method—relied on potential temperature measurements from an unmanned aerial vehicle (UAV) flights to validate our results. Our results indicate that a combination of the gradient method and δ-threshold method can provide better results, in terms of diurnal pattern, than using either method individually. Furthermore, the Haar wavelet and the Hybrid image processing can detect the PBL development comparably well, but both methods are dependent on their initial conditions and optimized algorithm settings. In addition, the accompanying UAV observations are conclusively shown to have a high degree of efficacy for validating the lidar data. This research highlights that a combination of PBLH determination methods can better describe the PBLH evolution throughout a day in some cases, while in others less common determination methods are proving useful, and a suite of retrieval methods should still be explored for precisely mapping the PBL in densely populated subtropical areas.
- NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future. - (see https://www.nasa.gov/uam-overview/ for more information) - The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). - The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and artificial intelligence capabilities. This presentation gives an overview of the Knowledge Graph and ChatGPT applications within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.
Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.
High altitude color and color infrared photography of the tri-county region of southeast Florida made it feasible to evaluate its potential for quantifying the dimensions of regional change. Attention was focused upon three main aspects of change in the region, which in fact overlap. These were; (1) the transformation of the southeast Florida wetlands; (2) the expansion of agriculture; and (3) the growth of the urbanized area. The development analyzed covered the period of thirteen years from 1956 to 1969. Results using this new 18 km photography were superior because of the degree of resolution, the combined power of color and color infrared interpretation, and the large area covered by each frame. The greatest advantage of high altitude imagery is the time-saving element, since it is possible to delineate and identify major geographic patterns over thousands of sq km very rapidly.
Ozone formation in urban smog is controlled by a complex set of reactions which includes radical production from photochemical processes, catalytic cycles which convert NO to NO2, and termination steps that tie up reactive intermediates in long-lived reservoirs. The reaction OH + NO2 + M -4 HONO2 + M (la) is a key termination step because it transforms two short-lived reactive intermediates, OH and NO2, into relatively long-lived nitric acid. Under certain conditions (low VOC/NOx), ozone production in polluted urban airsheds can be highly sensitive to this reaction, but the rate parameters are not well constrained. This report summarizes the results of new laboratory studies of the OH + NO2 + M reaction including direct determination of the overall rate constant and branching ratio for the two reaction channels under atmospherically relevant conditions.
Contributions of space exploration which are widely recognized are those dealing with the impact of space technology on public health and medical services in both urban and remote rural areas. Telecommunications, image enhancement, 3-dimensional image reconstructions, miniaturization, automation, and data analysis, have transformed the delivery of medical care and have brought about a new impetus to the field of biomedicine. Many areas of medical care and biological research have been affected. These include technological breakthroughs in such areas as: (1) diagnosis, treatment, and prevention of cardiovascular diseases, (2) new approaches to the understanding of osteoporosis, (3) early detection of genetic birth defects, (4) emergency medical care, and (5) treatment of chronic metabolic disorders. These are but a few examples where technology originally developed to support space medicine or space research has been applied to solving medical and health care delivery problems on Earth.
Frequency and intensity of warm and moist air-mass intrusions into the Arctic have increased over the past decades and have been related to sea ice melt. During our year-long expedition in the remote central Arctic Ocean, a record-breaking increase in temperature, moisture and downwelling-longwave radiation was observed in mid-April 2020, during an air-mass intrusion carrying air pollutants from northern Eurasia. The two-day intrusion, caused drastic changes in the aerosol size distribution, chemical composition and particle hygroscopicity. Here we show how the intrusion transformed the Arctic from a remote low-particle environment to an area comparable to a central-European urban setting. Additionally, the intrusion resulted in an explosive increase in cloud condensation nuclei, which can have direct effects on Arctic clouds’ radiation, their precipitation patterns, and their lifetime. Thus, unless prompt actions to significantly reduce emissions in the source regions are taken, such intrusion events are expected to continue to affect the Arctic climate.
Significance Increasing global population, urbanization, and climate change pose tremendous constraints on water availability. While membrane processes offer attractive, energy-efficient options, transformative advances in membrane technology are needed to enable reuse of poor-quality waters from diverse sources. This work brings fundamental understanding of solute–surface interactions to membrane phenomena, including selectivity and fouling. Molecular simulations reveal limitations of traditional predictive measures of solute–surface partitioning, suggesting alternative thermodynamic signatures for solute binding that highlight the contextual nature of hydrophobicity. An inverse design algorithm provides a tool to pattern membrane nanoscale chemistry to optimize solute affinity and selectivity. This work demonstrates how simulations can inform practical materials design for sustainable water treatment and, more broadly, many technologies involving water-mediated solute–surface interactions.
The rapid process of urbanization has led to water pollution, reduction of space areas and channel deposition. However, current river protection and management levels are not suitable for city development level and human demand. Therefore, these problems have not yet been solved. China is still in an era of rapid urbanization, which means that the influence of urbanization in rivers will increase in the future—and the task of river protection and management will be more arduous. In order to meet the challenges and opportunities brought by urbanization and promote the sustainable use and management of river, this study puts forward relevant suggestions on river protection and management, such as river management mechanisms, economic incentive instruments, technical support, transformation of development concepts, improving laws, regulations and policies and stimulating public participation.