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All sourcesNASA NTRSDOE OSTI
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 OSTI2025

Evaluating the Effectiveness of Soil Profile Rehabilitation for Pluvial Flood Mitigation Through Two-Dimensional Hydrodynamic Modeling

Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of a soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach for the Tiffany Run watershed, Baltimore City. This study utilized different extreme storm events, a high-resolution (1 m) LiDAR Digital Terrain Model (DTM), building footprints, and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, the City Catchment Analysis Tool (CityCAT), to simulate urban flood dynamics. The pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 m in most areas, with hydrologic soil groups C and D, especially downstream of the study area. The post-soil rehabilitation simulation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 m. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following the rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. The validation using a contingency matrix demonstrated true-positive rates of 0.75, 0.50, 0.64, and 0 for the selected events, confirming the model’s capability at capturing real-world flood occurrences.

Baltimore City↗
DOE OSTI2023

Substantially positive contributions of new particle formation to cloud condensation nuclei under low supersaturation in China based on numerical model improvements

New particle formation (NPF) and subsequent particle growth are important sources of condensation nuclei (CN) and cloud condensation nuclei (CCN). While many observations have shown positive contributions of NPF to CCN at low supersaturation, negative NPF contributions were often simulated in polluted environments. Using the observations in a coastal city of Qingdao, Beijing, and Gucheng in north China, we thoroughly evaluate the simulated number concentrations of CN and CCN using an NPF-explicit parameterization embedded in the WRF-Chem model. For CN, the initial simulation shows large biases of particle number concentrations at 10–40 and 40–100 nm. By adjusting the process of gas–particle partitioning, including the mass accommodation coefficient (MAC) of sulfuric acid, the phase changes in primary organic aerosol emissions, and the condensational amount of nitric acid, the improvement of the particle growth process yields substantially reduced overestimation of CN. Regarding CCN, secondary organic aerosol (SOA) formed from the oxidation of semi-volatile and intermediate-volatility organic compounds (S/IVOCs) is called SI-SOA, the yield of which is an important contributor. At default settings, the SI-SOA yield is too high without considering the differences in precursor oxidation rates. Lowering the SI-SOA yield under linear H 2 SO 4 nucleation scheme results in much-improved CCN simulations compared to observations. On the basis of the bias-corrected model, we find substantially positive contributions of NPF to CCN at low supersaturation (~ 0.2 %) over broad areas of China, primarily due to competing effects of increasing particle hygroscopicity, a result of reductions in SI-SOA amount, surpassing that of particle size decreases. The bias-corrected model is robustly applicable to other schemes, such as the quadratic H 2 SO 4 nucleation scheme, in terms of CN and CCN, though the dependence of CCN on SI-SOA yield is diminished likely due to changes in particle composition. This study highlights potentially much larger NPF contributions to CCN on a regional and even global basis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Wyandotte Integrated Renewable Energy Strategy (WIRES 2) (Final Technical Report)

Wyandotte Municipal Services (WMS), a Michigan Municipal Utility offering electric generation, transmission & distribution, water filtration & distribution and Broadband/Video sought to deploy a set of renewable energy projects for their Electric Utility that contributed to reducing the City’s environmental and energy impact, provided examples that would encourage businesses and citizens to adopt cleaner energy practices and help WMS meet the required renewable energy standards of the State of Michigan (10% in 2015, 15% in 2021). WMS generates and delivers roughly 280,000 megawatt hours (MWh) of electricity annually to more than 10,000 homes and 1,000 businesses within the City of Wyandotte. The Wyandotte Integrated Renewable Energy Strategy – WIRES 2 resulted in deployment of a set of community-scale micro-wind turbines, evaluation and installation of energy-efficient and low-maintenance light-emitting diode (LED) street lights and park lights in highly visible locations in the City and expansion of the geothermal heating and cooling district that already included residential geothermal systems, to include larger scale business/institutional systems at the Utility’s largest electric customer and at the City Library. Finally, WMS identified the need for job training in energy efficiency and renewable energy technologies to further develop a local skilled workforce in these areas. Partially funded by the grant, the wind turbine project provides a benchmark for WMS, local businesses and residents in the evaluation of utility-scale wind v. micro-wind in an urban environment as a renewable energy strategy. Also partially funded by the grant, the LED project benefited the City of Wyandotte by reducing load on the electric system for the public utility but also reduced City costs for streetlighting, parking lot lighting and park lighting without a compromise in security. The project also resulted in a visual success that has led to additional approvals for non-Grant related LED lighting installations resulting in further reduction in the electric load in the City. Commercial Geothermal installations resulted in the Utility being able to eliminate steam customers who formerly utilized the byproduct of steam from WMS electric production. Due to the reduction in natural gas prices and the overall reduction in market purchased power, it was no longer feasible for WMS to operate its Power Plant as a base load plant and to only operate on a market dispatch philosophy when market prices dictated that WMS could produce electricity cheaper than the market purchase price. This has resulted in fewer operating days for the WMS Power Plant, resulting in less environmental impact. WMS has now completely exited the steam business and the ability to offer a geothermal option to two (2) former steam customers (BASF-Wyandotte and the Bacon Memorial Library) allowed for that. In addition, peak loads have been shaved for these two (2) large customers due to the consistent temperature offered by geothermal heating and cooling, reducing the swings associated with thermostat changes caused by outside temperature changes during extreme high and low temperatures. Finally, energy efficiency and renewable energy college-level classes at Wayne County Community College allowed a group of interested students to pursue studies in those technologies through grant provided tuition, books and course materials. This effort provided a foundation of potential future employees not only for WMS but other energy efficiency/renewable energy employers such as Franklin Energy, a partner with WMS in energy optimization efforts with Wyandotte businesses.

17 WIND ENERGY↗
DOE OSTI2024

Data Driven Commercial Building Energy Code Compliance and Technology Inventory for New York City

Building Performance Standards (BPS) are gaining national traction. A BPS will require new processes in the design, construction, and operation of buildings that take the occupants into account and enable predictive analysis to ensure compliance with current and future GHG emissions caps. In New York City, most buildings over 25,000 square feet will be regulated by a BPS starting in 2024, regardless of whether it is new construction permitted under current energy codes or an existing building. This research is one of the first to begin the evaluation of a long-term series of building policies in the context of an open data ecosystem, in cooperation with city agencies. Existing building policies enacted in NYC have ranged from building energy benchmarking and labeling to energy audits to the regulation of GHG emission in buildings. Through the development of a dataset related to building technologies and energy consumption, this project can help to evaluate if meaningful conclusions can be drawn for the data that has been largely self-reported in compliance with city regulations. This project will also provide lessons learned from a deep dive into these types of datasets to provide best practices for municipalities or states seeking to embark on policies like those enacted in NYC. In addition, a Building Automation System (BAS) Stretch Standard of Care (SSOC) for owners, designers, and building operators will enable the measurement and predictive analysis of energy consumption and GHG emissions at the plant, system, or component level, in anticipation of regulated GHG limits on buildings based on energy use. The SSOC is expected to be suitable for use on a national level. The primary feature of an SSOC is a standardized format for a set of BAS points that can be used to control and to gather data from individual plants, systems, or components that are related to building energy consumption. This project examined how measurements compare to prescriptive or simulation-based energy code targets, finding little correlation between predictive 8760-hour energy modeling and actual energy consumption for a small sample (n=27) of buildings constructed after 2015. Other analysis found that, while large multifamily housing (MFH) buildings showed a general trend similar to predicted reductions in energy use from the implementation of model commercial energy codes, this trend was not evident in the office, K-12 school, and hotel use groups in NYC. No upward or downward trends in energy consumption were found when buildings were grouped by size. Energy audit data were analyzed and it appears that there is bias by audit company on measures recommended to clients. Further research should be performed to cross-analyze this with other attributes, such as building size, vintage, and number of stories. Analysis found that for 281 buildings that were permitted and completed after 2015 and had submitted benchmarking data in 2022, between 81% and 96% (by use group) were found to be in compliance with the 2024 to 2029 NYC BPS emission caps, and between 55% and 89% were in compliance with the 2030-2034 caps. This work is beneficial to the public in helping policymakers and building stakeholders better understand the wide-ranging implications of a BPS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health

Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.

Namgung, Min [ORNL]↗
DOE OSTI2022

3D Reality Energy Modeling Software

The team will combine Bentley Systems’ ContextCapture and extend Oak Ridge National Laboratory’s building energy modeling capabilities in order to create digital twins of buildings that allow simulation-informed improvement for energy efficiency and demand response in the design and operation of the built environment. Bentley Systems’ ContextCapture software processes data from 3D laser scanning and photographs (through photogrammetry) to create a photorealistic, 3D mesh with a scale of individual objects to building to city-sized. This capability easily provides city-level visualization models and platforms where sustainable design alternatives are readily evaluated. Oak Ridge National Laboratory (ORNL) serves the U.S. Dept. of Energy (DOE) as one of three core laboratories developing building energy modeling tools EnergyPlus and OpenStudio. ORNL’s AutoBEM software (bit.ly/AutoBEM) can process imagery (satellite, aerial, and street level), LiDAR, cartographic layers, tax assessor’s data, and other data sources to extract building footprints, height, window-to-wall ratio, building type, vintage, and other building properties. AutoBEM has created 178,368 building energy models that were empirically validated with 15-minute whole-building electrical data from a utility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cost Benefit Analysis of Solar PV for the City of Oldsmar, FL

The City of Oldsmar is in the early stages of exploring renewable energy opportunities as a part of their initiative to reduce energy costs, improve backup power capacity, and begin the transition to more sustainable operations. The city is particularly interested in deploying a floating or ground-mounted solar PV system at its wastewater treatment plant. Currently, there are no solar PV installations in Oldsmar and there has been hesitancy to implement such a project due to the possible capital costs and limited technical capacity. Despite this the initiative has gained internal buy-in among staff working for the city. To assist with gaining more buy-in and making future decisions the city sought technical assistance (TA) from the Energy to Communities (E2C) Expert Match Program. Through the Expert Match Program, the city received TA provided by the Pacific Northwest National Lab (PNNL) to evaluate the cost effectiveness of several solar options. This report details the analysis and results of the work performed for TA.

14 SOLAR ENERGY↗
DOE OSTI2021

Fossil fuel CO 2 emissions over metropolitan areas from space: A multi-model analysis of OCO-2 data over Lahore, Pakistan

Urban areas, where gathering more than 55% of the global population, alone contributed to more than 70% of anthropogenic fossil fuel carbon dioxide (CO 2ff ) emissions. Accurate quantification of CO 2ff emissions from urban areas is of great importance to the formulation of global warming mitigation policies to achieve carbon neutrality by 2050. Satellite-based inversion techniques are unique among “top-down” approaches, potentially allowing us to track CO 2ff emission changes over cities globally. However, its accuracy is still limited by incomplete background information, cloud blockages, aerosol contaminations, and uncertainties in models and emission inventories used as prior. To evaluate the current potential of space-based quantification techniques, we present the first attempt to monitor long-term changes in CO 2ff emissions based on the OCO-2 satellite measurements of column-averaged dry-air mole fractions of CO 2 (X CO2 ) over a fast-growing Asian metropolitan area: Lahore, Pakistan. We first examined the OCO-2 data availability at global scale. About 17% of OCO-2 soundings are marked as high-quality soundings by quality flags over the global 70 most populated cities over the period 2014-2019. Cloud blockage and aerosol contamination are the two main causes of data loss. As an attempt to recover additional soundings, we evaluated the effectiveness of OCO-2 quality flags at the city level by comparing three flux quantification methods (WRF-Chem, X-STILT, and flux cross-sectional integration method), all based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) product. The satellite/bottom-up emissions (OCO-2/ODIAC) ratios of the high-quality tracks better converged across the three methods compared to the all-data tracks with reduced uncertainties in emissions. Thus, OCO-2 quality flags are useful filters of low-quality OCO-2 retrievals at local scales, although originally designed for global-scale studies. All three methods consistently suggested that the ratio medians are greater than 1, which implies that the ODIAC slightly underestimated the CO2ff emissions over Lahore. Additionally, our estimation of the a posteriori CO2ff emission trend was about 734 kt C/year (i.e., an annual 6.7% increase). 10,000 Monte Carlo simulations of the Mann-Kendall upward trend test showed that less than 10% prior uncertainty for 8 tracks (or less than 20% prior uncertainty for 25 tracks) is required to achieve a greater-than-50% trend significant possibility at a 95% confidence level. It implies that the trend is driven by the prior and not due to the assimilation of OCO-2 retrievals. The key to improving the role of satellite data in CO 2 emission trend detection lies in collecting more frequent high-quality tracks near metropolitan areas to achieve significant constraints from X CO2 retrievals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Estimating Energy Bounds for Adoption of Shared Micromobility

Shared micromobility has garnered widespread popularity in recent years, but limited attention has been given to the energy impacts of trips replaced by micromobility. This paper investigates the energy bounds of shared micromobility adoption. Travel demand data at the national and city level were analyzed to identify trips that can be served through micromobility, and scenarios with varying levels of micromobility adoption were evaluated. Results show that peak adoption of shared micromobility can reduce energy consumption from reported passenger travel by 1% at the national level and 2.6% at the city level, with micromobility-induced transit trips identified as the largest contributor for energy reduction. Sensitivity analysis was carried out to show how the energy impacts would change with various levels of key micromobility-related parameters, and results show distance threshold having a stronger influence on the energy impacts, compared to redistribution energy intensity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Improving energy efficiency while reducing anthropogenic heat from buildings: how retrofits influence the building stock and urban microclimate in Los Angeles

Anthropogenic heat (AH) from buildings contributes to urban overheating, especially during heat waves, yet building retrofit studies usually evaluate energy savings without assessing impacts on AH. This study quantifies how common building retrofit measures affect both building energy use and AH emissions across the City of Los Angeles. Using a bottom-up urban building energy modeling framework coupled with high-resolution local weather from the Weather Research and Forecasting model with Building Effect Parameterization (WRF-BEP), we evaluate eleven retrofit measures and two multi-measure retrofit packages. HVAC and LED lighting retrofits provide the largest city-wide annual site energy savings, while roof coating is most effective for reducing AH. A package optimized for energy savings reduces summer site energy use by about 32% (2.3 TWh), while a package incorporating AH-focused measures reduces the total AH by over 50% (137 PJ) with minimal difference in energy savings. The AH-aware package produces substantially greater urban cooling, reducing mean near-surface air temperature by up to 0.62 ℃ and peak temperature by up to 3.79 ℃. These results show that retrofit strategies selected only for energy savings may overlook major opportunities for urban heat mitigation. The study provides a framework for integrating AH into building retrofit planning and urban heat resilience policy.

Anthropogenic heat↗
DOE OSTI2021

Italian prototype building models for urban scale building performance simulation

Urban building energy modeling (UBEM) seeks to evaluate strategies to optimize building energy use at urban scale to support a city's building energy goals. Prototype building models are usually developed to represent typical urban building characteristics of a specific use type, construction year, and climate zone, as detailed characteristics of individual buildings at urban scale are difficult to obtain. This study investigated the Italian building stock, developing 46 building prototypes, based on construction year, for residential and office buildings. The study included 16 single-family buildings, 16 multi-family buildings, and 14 office buildings. Building envelope properties and heating, ventilation, and air conditioning system characteristics were defined according to existing building energy codes and standards for climatic zone E, which covers about half the Italian municipalities. Novel contributions of this study include (1) detailed specifications of prototype building energy models for Italian residential and office buildings that can be adopted by UBEM tools, and (2) a dataset in GeoJSON format of Italian urban buildings compiled from diverse data sources and national standards. Finally, the developed prototype building specifications, the building dataset, and the workflow can be applied to create other building prototypes and to support Italian national building energy efficiency and environmental goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Generation of Synthetic Data for a Radiation Detection Algorithm Competition

This paper details the generation of synthetic radiation data using large-scale Monte Carlo transport models to evaluate radiation search detection algorithms. Modular 3-D Monte Carlo models spanning multiple city blocks were constructed, loosely based on downtown Knoxville, TN, containing buildings composed of multiple materials (brick, granite, and concrete), sidewalks, a four-lane road, side streets, parking lots, and grassy fields. Background and simulated source detector response calculations from these models were used to create synthetic list mode data sets for a 2"×4"×16" NaI(Tl) detector moving through a city street at a constant speed. For the background simulations, major isotopes were computed individually so that background composition and variability could be computed efficiently outside Monte Carlo. The source detector response included six simulated sources placed at 15 source locations. Detector response was developed to be periodic through the city street so that a detector path could begin at one end of the model and wrap around to the other. This framework allowed for the creation of diverse data sets, each with its own unique background and simulated source detector response. Synthetic data allows for high-quality labels, which are useful in developing data-driven radiation detection algorithms. This methodology was used to create synthetic data sets which were released as part of a public data competition to spur the development of new radiation detection algorithms for radiological search applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Using SWMM for emergency response planning: A case study evaluating biological agent transport under various rainfall scenarios and urban surfaces

To assist in emergency preparedness for a biological agent terrorist attack or accidental pathogen release, potential contaminant levels and migration pathways of spores spread by urban stormwater were evaluated using a Storm Water Management Model (SWMM) of U.S. Coast Guard Base Elizabeth City, North Carolina. The high temporal-spatial resolution SWMM model was built using spore concentrations in stormwater runoff from asphalt, grass, and concrete collected from a point-scale field study. The subsequent modeled contamination scenarios included a notional plume release and point releases mimicking the field study under three rainfall conditions. The rainfall scenarios included a 6-hour natural rainfall event on Dec. 8, 2021 and two design storms (2-year and 100-year events). The observed spore concentrations from asphalt and concrete from the actual field experiment were applied to calibrate the washoff parameters in the SWMM model, using an exponential washoff function. The calibrated washoff coefficient (c 1 ) and exponent (c 2 ) were 0.01 and 1.00 for asphalt, 0.05 and 1.45 for grass, and 2.45 and 1.00 for concrete, respectively. The calibrated SWMM model simulated spore concentrations in runoff at times and magnitudes similar to the field study data. In the point release modeled scenario, the concrete surface generated 55.6% higher average spore concentrations than asphalt. Similarly, in the field experiment, a 175% (p < 0.05) higher average spore concentration in surface runoff was observed from concrete than from asphalt. Here, this study demonstrates how SWMM may be used to evaluate spore washoff from urban surfaces under different precipitation amounts, intensities, and durations, and how visualized spatial migration pathways in stormwater runoff may be used for emergency planning and remediation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Equitable Urban Electric Vehicle Charging: Feasibility and Benefits of Streetlight Charging in Kansas City Right-of-Way

With an increasing global emphasis on sustainability, electric vehicles (EVs) play a crucial role in reducing urban pollution and carbon emissions. For EVs to be widely adopted, equitable and convenient access to charging infrastructure is essential. Equity in this paper refers to the proactive engagement with the community to ensure that the benefits of streetlight charging are distributed equitably across diverse neighborhoods in Kansas City, providing fair charging opportunities and resources to all community members. This research explores the potential of utilizing streetlights—ubiquitous elements of urban electrical infrastructure—as low-cost, equitable EV charging solutions. Compared with conventional chargers, streetlight charging offers several notable advantages, including proximity to roadways, potential boosts to the local economy, and easier usage due to city ownership. These chargers also leverage existing power setups to minimize costs and maximize efficiency by reutilizing existing structures. In this manuscript, we introduce a systematic framework to develop, analyze, and evaluate a scalable and cost-effective streetlight charging solution. Initially, we employ a two-tiered site selection framework that accounts for both charging demand and equity considerations to identify optimal locations for streetlight chargers. We then assess the feasibility of these chargers through observational data from 23 units installed in Kansas City, Missouri, comparing their performance with traditional chargers. Here, our evaluation extends to the environmental impact, comparing reduced gasoline consumption and greenhouse gas (GHG) emission reductions between streetlights and regular charging stations. Our findings reveal that streetlight charging stations offer faster charging speeds than conventional stations, validating their viability for EV charging. Additionally, the duration of stay at streetlight chargers tends to be shorter, likely due to parking constraints or associated costs. Furthermore, streetlight-powered chargers demonstrate considerable environmental advantages, achieving 11.94% greater avoided gasoline consumption and 11.24% higher GHG reductions compared with regular charging stations, highlighting their substantial environmental benefits.

42 ENGINEERING↗
DOE OSTI2022

Performance Evaluation of the Meteorology and Air Quality Conditions From Multiscale WRF-CMAQ Simulations for the Long Island Sound Tropospheric Ozone Study (LISTOS)

The Long Island Sound (LIS) Tropospheric Ozone Study was a multi-agency collaborative field campaign conducted during the summer of 2018 to improve the understanding of ozone chemistry and transport from New York City to areas downstream, especially the LIS and adjacent Connecticut coastline. Measurements made during this campaign were leveraged to test and evaluate the coupled WRF-CMAQ model at 12 km, 4 and 1.33 km horizontal grid spacing. Special attention was placed on the model's representation of sea breeze circulations, low level jets, and boundary layer evolution. The evaluation suggests using higher resolutions resulted in improved surface meteorology statistics throughout the whole summer, with temperature biases seeing the biggest statistical improvements when using 1.33-km grid spacing, going from –0.12 to 0.08 K. Additionally, 4-km grid spacing provided the biggest advantage when simulating ozone over the region of interest, with biases being reduced from 2.40 to 0.57 to 0.37 ppbV with increased resolution. Case studies of two high ozone concentration events (July 10 and August 6) revealed that sound breezes and low-level jets had a critical role in transporting pollutant-rich, shallow marine air masses from the LIS inland over the Connecticut coast. Modifications were made to the representation of sea surface temperatures, which subsequently improved the simulation of surface ozone predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Framework for Modeling 3D-Printed Concrete Construction to Assess Energy Efficiency and Backup Power Trade-Offs in a Mixed-Use, New Construction Neighborhood Development

This paper presents a framework that expands the URBANopt(TM) modeling platform to include 3D-printed concrete wall assemblies and assess energy efficiency and backup power trade-offs in new housing developments. Applied to a planned mixed-use neighborhood in Oil City, Pennsylvania, the workflow integrates building energy and distributed energy resource (DER) modeling to evaluate envelope and equipment upgrades alongside DER operations. Results show that advanced 3D-printed envelopes combined with efficient systems and onsite photovoltaics (PV) and storage reduce energy use intensity and sustain critical loads during outages. The framework supports planning for emerging construction technologies by quantifying key trade-offs between energy efficiency and backup power performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗