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At least 55 records · Page 3

An approach to urban waterway assessment using holistic values and reciprocity

Current aquatic ecosystem assessment methods and tools often focus on physical, chemical, and biological indicators of ecosystem health. This approach to ecosystem assessment is not always straightforward to execute in urban environments and ignores potential connectivity between social and environmental outcomes. During a workshop at the Symposium on Urbanization and Stream Ecology in Brisbane, Australia in 2023 (SUSE6), we developed an approach to urban aquatic ecosystem assessment that incorporates a holistic perspective. Specifically, our approach considers both environmental (biological, chemical, and physical integrity) and social (community connection, human safety, resource use) values of urban waterways. This approach is inclusive of Indigenous perspectives, such as the concept of reciprocity, whereby consideration of both the environment and society leads to a healthy ecosystem. Here, to highlight how this holistic assessment approach could be used, we present real-world examples that included assessing both environmental and societal values informed by reciprocity or balanced perspectives. This approach can be broadly applied and adapted to specific aquatic ecosystem conditions and projects, providing an inclusive, community-centered approach for assessing the health of waterways in urban environments.

Indigenous knowledge↗

Urban morphology from a landscape perspective: How building morphology distribution land models (BMDLM) emulate pattern and process

Urban form (e.g., building morphology such as height or footprint) can be used to predict environmental footprints, such as energy/water consumption and carbon emissions. Although progress has been made in predicting building characteristics to fill gaps in observation or derive 3-D representations, the relationships between morphology and other variables such as land use and population are poorly understood. Understanding these relationships may enable projections for how cities will evolve with landscapes in the future. A suite of random forest models, the Building Morphology Distribution Land Models (BMDLM), was developed to determine how well building morphology for two distinct statistical measures (central tendency and frequency) can be predicted using land use (e.g., zoning) and population at different resolutions. Clark County, Nevada and Los Angeles County, California are explored as case studies. Generally, 1-km models outperformed 30-m models. Frequency distribution models had the best performance, especially in LA County. Frequency models significantly outperformed spatial autocorrelative models using inverse distance weighting (IDW). BMDLM offers a new take on modeling urban form in which generalized landscape patterns are characterized to understand the influence of population and zoning on urban development, as described by urban scaling theory.

Sturtevant, Jillian [Baylor Univ., Waco, TX (Unite↗

The birth of a field through a marriage of scales: urban meteorological modeling meets regional climate modeling

Urban meteorological modeling and regional climate modeling developed largely independently, with each discipline addressing different aspects of atmospheric processes across space and time. In this review, I show that urban climate modeling did not arise as a simple scaling extension of urban meteorological modeling or an add-on to regional climate modeling, but instead emerged through the selective inheritance of complementary strengths from its parents after both disciplines reached sufficient methodological maturity. By tracing the parallel evolution of these disciplines, I demonstrate that urban climate modeling inherited the physically explicit treatment of the built environment developed within urban meteorological modeling, and the hierarchical scale translation and climatological framing that matured within regional climate modeling, allowing urban effects to influence climate-relevant outcomes. Building on this synthesis, I propose an objective, and methodologically-grounded definition of urban climate modeling that distinguishes it from urban meteorological modeling. This distinction is increasingly important as urban climate data informs decisions with long-lived societal consequences, and as ambiguity in terminology risks conflating fundamentally different modeling frameworks with distinct physical meaning and decision relevance. In addition to a clarifying definition, this review outlines a research framework for advancing urban climate modeling through scale-aware coupling strategies that preserve physically consistent urban-atmosphere interactions.

regional climate↗

Artificial light at night outweighs temperature in lengthening urban growing seasons

Plant growing seasons are largely regulated by light and temperature. Cities are increasingly hot (higher air temperature), from the urban heat island effect, and bright (artificial light at night, ALAN). However, the relative effect of heat and light on the timing of plant growth events, called phenology, is unclear, limiting our understanding under climate change and urbanization. Here we used multiple satellite observations of 428 Northern Hemisphere cities from 2014 to 2020 to analyze phenological patterns along a gradient from rural to urban. We found that ALAN increased exponentially toward urban centers, and exerted stronger influence than air temperature in lengthening the urban growing season, especially by delaying its end, although the effects varied across climate zones. Our findings demonstrate that ALAN is a critical driver of vegetation dynamics in cities, one we should consider during urban management and development.

Wang, Lvlv [Wuhan Univ. (China)] (ORCID:0000000347↗

bhartendupandey/Urban-Infrastructure-Inequalities: Urban Infrastructure Inequalities Analysis Code

This code release contains the final scripts used in the multi-scale analysis of infrastructure inequalities, as reported in the manuscript (Pandey et al. 2025). Reference: Pandey, B., Brelsford, C., & Seto, K. C. (2025). Rising infrastructure inequalities accompany urbanization and economic development. Nature Communications, 16(1), 1193.

Pandey, Bhartendu [ORNL] (ORCID:0000000237125961)↗

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↗

Differences in urban plant community compositions across an urban-rural gradient in Knoxville, TN

Urban forests, or vegetation in areas under heavy human influence, provide many ecosystem services to urban residents such as localized cooling via evapotranspiration, shade, filtering of air pollution, and the associated health benefits of natural spaces. In order to quantify the magnitude of localized cooling by trees growing in varying levels of urbanization (based on % impervious surfaces, e.g., buildings, pavement), urban forest species composition, tree size, and tree density must be characterized. As a part of Oak Ridge National Laboratory’s (ORNL) urban forest temperature study, we conducted tree censuses in five Knoxville city parks where ORNL meteorological stations are deployed. Moreover, we measured every woody plant ≥ 5 cm diameter at breast height (DBH) within a 50 m radius of each site’s meteorological station for its DBH and species identification. When possible, individuals were identified down to species. Certain genera (Quercus spp., Carya spp., Pinus spp.) were identified down to genera in interest of time. Individual and total site basal area were calculated from measured DBH data. Results show notable differences in urban plant community compositions and total woody plant basal area across sites, with more urban sites closer to downtown (West View and SEEED) having lower tree basal area than the more suburban sites (West Hills, Cumberland Estates, and Victor Ashe). We identified 54 species across all sites, with West Hills and Victor Ashe having the highest species diversity. Our results show differences in forest compositions and sizes across Knoxville, which are currently informing ORNL’s evapotranspiration estimates for each site. Data Summary: Census data for West Hills (WH), Cumberland Estates (CE), Victor Ashe (VA), West View (WV), and Socially Equal Energy Efficient Development or SEEED (SD) urban forests in Knoxville, TN, USA, including tree size based on diameter at breast height (DBH; 1.3 m), species identification (Latin and common names), and basal area per stem (BA=π×[.5*DBH]^2). Field data are summarized in this file: “Community_Composition_Data.CSV”. Site-specific data detailing each site’s coordinates, number of stems measured at DBH, average tree DBH, α-diversity (number of species present), and total site basal area (sum of individual basal areas per site) are in this file: “Site_Comparisons.CSV”.

Warren, Jeffrey [ORNL] (ORCID:0000000206804697)↗

Importance of Spatially Continuous Urban Surface Properties in Urban‐Resolving Earth System Modeling

Accurate representation of urban properties and processes at higher resolutions in global modeling systems is essential for advancing our ability to capture the complexities of urban systems and informing effective resilience strategies. However, the prescription of coarse global-scale urban properties in most state-of-the-art Earth system models (ESMs) is limiting their potential for capturing urban signals as they advance toward kilometer-scale simulation capabilities. To bridge this gap in inadequate urban property representation and to advance urban-resolving Earth system modeling, this work integrates the newly-developed global 1 km-resolution facet-level urban surface property data set, U-Surf, into the land component of Community Earth System Model (CESM)—Community Terrestrial System Model (CTSM). The land-only CTSM simulations are validated against satellite measurements, ground-based urban weather stations, flux tower observations, and reanalysis data. Results demonstrate that the enhanced urban properties allow improved simulations of urban meteorology and surface energy fluxes compared to the default coarse-resolution categorical urban canopy parameters. Spatial scaling analysis reveals regime-dependent information loss during resolution aggregation, as well as substantial scale-dependent variations in urban surface energy flux representation. Furthermore, these findings have critical implications for coupled Earth system modeling when including the effect of land-atmosphere interaction. This work establishes a foundation for future urban-resolving kilometer-scale ESM development, which will enable systematic intra- and inter-city comparisons that inform urban adaptation strategies across diverse global urban environments.

Cheng, Yifan [University of Illinois Urbana-Champa↗

Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization

The study proposes a comprehensive Transportation Mobility (TransitMo) framework covering conceptual design, model formulation, optimization, simulation, and impact analysis of the transportation mobility system. TransitMo is composed of a transportation digital twin developed in Simulation of Urban MObility (SUMO) and an Intelligent Traffic Management and Control Center (ITMCC) that identifies the best ways to improve the movement of people within urban areas using various modes of transportation. This study encompasses advanced modeling techniques, algorithms, and strategic testing to optimize energy efficiency and mobility in a multimodal shared mobility network. TransitMo’s practical applications are exemplified through a city-scaled simulation network in Chattanooga, TN, employing demographic data to analyze historical traffic patterns and forecast future demands. Central to this methodology are three models: the User Preference Model (UP), the Energy Consumption Model (EC), and the System Optimization Model (SO). These models work in concert to iteratively devise the optimal travel incentives and minimize the total system cost in a real-time manner. In conclusion, test results verified that the proposed adaptive incentive program and optimized bus scheduling can improve network performance by increasing public transit ridership.

42 ENGINEERING↗

New Particle Formation and Growth in the Houston Atmosphere During TRACER (Final Report)

From 2020-2025, researchers from UC Irvine, UC Riverside, and Colorado State University collaborated on a Department of Energy-funded project to understand how airborne particles form and grow in urban atmospheres, conducting an intensive field campaign in Houston, Texas during summer 2022. Using advanced instruments to measure gas-phase chemicals, particle composition, and a specialized chamber to study particle growth, the team discovered that sulfur-containing compounds from industrial and power plant emissions are the dominant driver of new particle formation in Houston, with particles typically forming locally in the city and growing as air moves away in the urban plume. The research revealed an important methodological insight: measurements from fixed ground stations can be misleading when interpreting how particles actually evolve as air masses move, which has significant implications for how scientists worldwide interpret atmospheric observations. These findings improve understanding of urban air quality and help reduce uncertainties in climate models, since these particles play critical roles in cloud formation and Earth's radiation balance, while also providing detailed information about ultrafine particle composition relevant to public health. The project trained three doctoral students, developed enhanced computer models for urban particle formation, and made all data publicly available through the DOE Atmospheric Radiation Measurement data archive for use by the broader scientific community.

54 ENVIRONMENTAL SCIENCES↗

From roads to roofs: How urban and rural mobility influence building energy consumption

In this article, understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Exploring urban typologies using comprehensive analysis of transportation dynamics

Abstract As urban areas continue to expand and develop, categorizing cities into typologies offers a valuable framework for understanding metropolitan dynamics and fostering inter-city collaboration. However, existing typologies related to urban mobility have limitations, failing to consider cities within a single large urban region and often overlooking crucial dimensions such as trip demand and traffic flow. In this paper, we introduce a transportation-focused characterization for cities within a large urban region, specifically the San Francisco Bay Area, California. We incorporate over 40 metrics across five transportation dimensions: trip demand, road network, multi-modal network, traffic flow, and land use. Specifically, for the trip demand dimension, we include metrics capturing residents’ trip characteristics, such as mode share, intra-city trips, and inter-city trips. Additionally, we analyze the purpose of trips entering the city to gain a deeper understanding of incoming trip patterns. In the traffic flow dimension, we examine metrics like vehicle miles traveled, delay, and congestion to assess the traffic conditions on the street network. These, combined with other dimensions, provide a comprehensive view of a city’s transportation dynamics. Using unsupervised machine learning clustering methods, we identified eight distinct typologies for the Bay Area: Live Work Cities; Job and Activity Magnet Cities; Anchor Cities; Multi-modal Cities; Hyper-connected Cities; Low-density Residential Cities; Medium-density Residential Cities; and Mixed-use Residential Cities. Our findings show that many clusters are strongly influenced by trip demand and traffic flow metrics. Finally, we examine the practicality of this typology and its potential to guide collaborative transportation management strategies. The typologies provide a foundation for dialogue among Bay Area cities, focusing on evaluating shared characteristics and leveraging successes or challenges to develop unified strategies for transportation management.

Kuncheria, Anu↗

Model America - 2022 Arizona Building Energy Simulation Results from ORNL's AutoBEM

This dataset contains energy simulation outputs using ORNL's AutoBEM, covering the summer months (June 1st to August 31st) (named by "county_name.csv") and full-year periods (in output_all_year.zip) for each county in Arizona, USA. The data package includes simulation results such as basic energy metrics and anthropogenic emissions estimates. Files are provided in .csv. These data were generated to analyze the impact of weather conditions on energy use and emissions across urban and rural environments in Arizona, aiming to support research on urban heat islands, energy efficiency, and building retrofitting strategies. The source data for this work include NASA POWER weather datasets and computational models run using AutoBEM (i.e., an automated, large-scale energy simulation tool leveraging OpenStudio/EnergyPlus). This dataset can assist researchers, urban planners, and policymakers in developing climate-resilient energy systems and understanding anthropogenic contributions to local environments.

54 ENVIRONMENTAL SCIENCES↗

More Heavy Precipitation in World Urban Regions Captured Through a Two‐Way Subgrid Land‐Atmosphere Coupling Framework in the NCAR CESM2

Abstract Current global climate models (GCMs), limited to grid‐scale land‐atmosphere coupling, cannot represent subgrid urban‐rural precipitation contrasts. This study develops an innovative two‐way subgrid land‐atmosphere coupling framework in the National Center for Atmospheric Research (NCAR) Community Earth System Model version 2 (CESM2) to explicitly resolve land‐atmosphere interaction over subgrid individual land units. Results show that urban heat island (UHI) leads to the urban rainfall effect (URE), which in turn alleviates overestimated UHI over China in CESM2. The URE manifests as a shift toward more heavy precipitation and less light precipitation in world urban areas than in surrounding rural counterparts. This feature is consistent with available observations. In heavy precipitation situations, the UHI promotes atmospheric instability and enhances atmospheric water vapor holding capacity, resulting in more heavy precipitation in urban areas. Conversely, in light precipitation situations, the UHI and decreased evaporation from urban impermeable surfaces diminish atmospheric relative humidity, suppressing light precipitation.

Geology↗

Building a Healthy Urban Design Index (HUDI): how to promote health and sustainability in European cities

As global urbanisation accelerates, alongside declining environmental quality and increasing climate challenges, it is increasingly vital for urban planners and policy makers to integrate health and wellbeing considerations into urban planning. This study introduces the Healthy Urban Design Index (HUDI), a high-resolution spatial index developed for European cities. HUDI combines policy-relevant indicators related to urban design, sustainable transportation, environmental quality, and greenspace accessibility—key factors influencing human health and well-being. Unlike existing indices, which often focus on few or large metropolitan cities and lack spatial granularity, HUDI offers high resolution and extends its scope to small-sized and medium-sized cities, home to over 50% of Europe's population.

60 APPLIED LIFE SCIENCES↗

Community-Informed Urban Flood Modeling for Impact Mitigation

The intensification of the hydrologic cycle due to climate change poses a threat to aging and under-designed water infrastructure systems which cannot adequately manage intense storm events. Developing a comprehensive plan for managing rain-driven flooding events is challenging due to uncertainties in the magnitude and frequency of future storm events and conflicting stakeholder objectives. In the City of Baltimore, Maryland, stormwater infrastructure is struggling to keep up with rainfall-driven (pluvial) flooding events, which regularly damage housing and disrupt transportation for residents. In this study, a hybrid of community engagement, numerical modeling, and artificial intelligence techniques are employed to explore prospective urban flooding adaptations. Community engagement drives the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed. The model integrates complex surface and subsurface stormwater infrastructure data from the City, high-resolution spatial data, insights from local public works experts, and the lived experiences of City residents. This co-developed model simulates adaptations of interest to stakeholders in the city, including green and grey infrastructure and operational management strategies. Stormwater management scenarios focused on inlet cleaning and spatially concentrated green infrastructure are found to be the most effective in reducing flood depths in community priority locations. Together, these adaptations can reduce the duration of intersection inundation by more than twenty minutes, allowing for quicker emergency response and restoration of typical transportation systems. Future work will combine this community engaged flooding model with the Deep Uncertainties Pathways framework to explore tradeoffs between adaptations and develop dynamic adaptations which align with community objectives, enhance climate resilience in Baltimore, and can be adjusted in response to changing future conditions.

Ava, Spangler [Pennsylvania State University]↗

Effectiveness of nature-based solutions to reduce flooding in Quad Cities Metro Area (QCMA) using SWMM-HEC based flood model

Nature-based solutions (NbS) have gained significant attention as strategies for addressing urban environmental challenges, particularly since the establishment of the UN Sustainable Development Goals (SDGs) for 2030. However, the current research on NbS for urban flood management lacks comprehensive methodological approaches for identifying suitable areas and evaluating their effectiveness across different urban settings. Here, this study attempts to fill this gap by proposing a methodological framework integrating multi-criteria analysis with a SWMM-HEC-based hydrologic and hydraulic (HH) model to assess the suitability of NbS for the Quad Cities Metro Area (QCMA), consisting of Davenport, Bettendorf, Moline, and Rock Island. Eight NbS options-green roofs, rain gardens, infiltration trenches, permeable pavements, vegetative swales, dry detention basins, retention ponds, and rain barrels/cisterns - were considered based on volumetric efficiency and runoff reduction efficiency. The study reveals that implementing the proposed NbS could have substantially reduced flood depths in key historical flood events by 21% in 1993, 15% in 2008, 16% in 2011, 23% in 2014, 40% in 2019, and 10% in 2023. The findings highlight a critical trade-off between peak runoff and NbS implementation: while NbS effectively reduce flood impacts, they also enhance volumetric efficiency by approximately 43%. In high-density areas of the QCMA, flood depth reductions of around 20% suggest that NbS are a viable solution for dense urban environments with limited space. This shows the potential for integrating NbS into existing infrastructure, offering a promising approach for cities facing increasing flooding risks. The proposed methodology provides a practical framework for incorporating NbS into urban stormwater management, addressing gaps in optimizing NbS performance, and offering a pathway to scale their application in other urban areas with various environmental and social contexts.

CMIP6↗

The Urban Deployment Model: A Toolset for the Simulation and Performance Characterization of Radiation Detector Deployments in Urban Environments

Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging, as it does not depend solely on the detector capabilities themselves. Many factors must be taken into consideration, including specific source and background signatures, the topology and constraints of the deployment environment, the presence of nuisance sources, and whether detectors are mobile or static. When considering the simultaneous deployment of multiple, heterogeneous detectors, assessment of the system-wide performance requires the simulation of the individual detectors, and a system-level analysis of the detection performance. In radiological source search-and-tracking scenarios, performance is mostly dominated by the probability of encounter, which depends on the specifics of a given deployment, e.g., static vs. mobile detectors or a combination of both modalities, the number of detectors deployed, the dynamic vs. static setting of false alarm rates, and individual vs. networked operation. The Urban Deployment Model (UDM) toolset was specifically developed to cover the gap in the available generic frameworks for the simulation of radiation detector deployments at city scales. UDM provides a unified and modular framework to support the simulation and performance characterization of heterogeneous detector deployments in urban environments. This paper presents the key components along the UDM workflow.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗