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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Assessing thermal resilience of an assisted living facility during heat waves and cold snaps with power outages

Extreme hot and cold weather events are becoming more frequent, intense, and longer due to climate change. When these events occur coincidentally with power outages, the resulting extreme indoor temperatures pose a severe health hazard for occupants. This study conducted a holistic modeling and analysis of an assisted living facility, where senior residents live, to assess its thermal resilience performance under a six-day heat wave in 2015 and a three-day cold snap in 2021 with power outages. Impacts of 13 energy efficiency measures on thermal resilience and backup power capacity of the facility were evaluated. Three thermal resilience metrics: the SET (standard effective temperature) degree-hours, the Heat Index, and the Hours of Safety, were used and calculated from the EnergyPlus simulation models. Furthermore, major findings are: (1) the facility would suffer from extreme temperatures during the cold and hot events without a power supply, not meeting the passive survivability requirements; (2) most passive envelope measures improve thermal resilience for both hot and cold events, but making the building envelope airtight results in conflicting performance between the hot and cold events; (3) natural ventilation is an effective measure to mitigate summer indoor overheating; and (4) the energy efficiency package can reduce backup power capacity by 19% for the three-day cold snap. It is recommended that building technologies and design strategies be evaluated to consider co-benefits of energy use, thermal resilience, and backup power needs through building energy codes or policies for existing and new buildings, which are transitioning for decarbonization and climate resilience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Parameter estimation with maximal updated densities

A recently developed measure-theoretic framework solves a stochastic inverse problem (SIP) for models where uncertainties in model output data are predominantly due to aleatoric (i.e., irreducible) uncertainties in model inputs (i.e., parameters). The subsequent inferential target is a distribution on parameters. Another type of inverse problem is to quantify uncertainties in estimates of “true” parameter values under the assumption that such uncertainties should be reduced as more data are incorporated into the problem, i.e., the uncertainty is considered epistemic. A major contribution of this work is the formulation and solution of such a parameter identification problem (PIP) within the measure-theoretic framework developed for the SIP. The approach is novel in that it utilizes a solution to a stochastic forward problem (SFP) to update an initial density only in the parameter directions informed by the model output data. In other words, this method performs “selective regularization” only in the parameter directions not informed by data. Further, the solution is defined by a maximal updated density (MUD) point where the updated density defines the measure-theoretic solution to the PIP. Another significant contribution of this work is the full theory of existence and uniqueness of MUD points for linear maps with Gaussian distributions. Data-constructed Quantity of Interest (QoI) maps are also presented and analyzed for solving the PIP within this measure-theoretic framework as a means of reducing uncertainties in the MUD estimate. We conclude with a demonstration of the general applicability of the method on two problems involving either spatial or temporal data for estimating uncertain model parameters. The first problem utilizes spatial data from a stationary partial differential equation to produce a MUD estimate of an uncertain boundary condition. The second problem utilizes temporal data obtained from the state-of-the-art ADvanced CIRCulation (ADCIRC) model to obtain a MUD estimate of uncertain wind drag coefficients for a simulated extreme weather event near the Shinnecock Inlet located in the Outer Barrier of Long Island, NY, USA.

42 ENGINEERING↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
DOE OSTI2022

Leveraging SETS resilience capabilities for safe-to-fail infrastructure under climate change

As the rehabilitation of infrastructure is outpaced by changes in the profile, frequency, and intensity of extreme weather events, infrastructure’s service disruptions and failures become increasingly likely. Safe-to-fail approaches for infrastructure planning and design improve the capacity of cities to adapt for uncertain climate futures by identifying social, ecological, and technological systems (SETS) capabilities to prepare for potential failure scenarios. In this work, we argue for transforming infrastructure planning and design to effectively utilize safe-to-fail approaches by navigating the opportunities and trade-offs of SETS resilience capabilities. From a technological vantage point, traditional infrastructure planning approaches account for social and ecological domains as external design conditions rather than embedded system characteristics. Safe-to-fail approaches directly challenge the isolation of the technological domain by necessitating a recognition that SETS domains are interconnected and interdependent in infrastructure systems, as such risks and system capabilities for resilience must be managed cohesively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A simple and fast algorithm for estimating the capacity credit of solar and storage

Energy storage is a leading option to enhance the resource adequacy contribution of solar energy. Detailed analysis of the capacity credit of solar energy and energy storage is limited in part due to the data intensive and computationally complex nature of probabilistic resource adequacy assessments. This paper presents a simple algorithm for calculating the capacity credit of energy-limited resources that, due to the low computational and data needs, is well suited to exploratory analysis. Validation against benchmarks based on probabilistic techniques shows that it can yield similar insights. The method is used to evaluate the impact of different solar and storage configurations, particularly with respect to the strategy for coupling storage and solar photovoltaic systems. Furthermore, application of the method to a case study of utilities in Florida, where solar is rapidly growing and demand peaks in the winter and summer, demonstrates that it can improve on rules of thumb used in practice by some utilities. If storage is required to charge only from solar, periods of high demand driven by cold weather events accompanied by lower solar production can result in a capacity credit of solar and storage that is less than the capacity credit of storage alone.

14 SOLAR ENERGY↗
DOE OSTI2025

Cataloging US state policy patterns towards microgrid deployment

Frequent extreme weather events have called for rigorous and timely efforts for alternative non-wire solutions. These efforts are getting more widespread to offer a perfect alternative as the conventional grid becomes progressively less resilient. One of these solutions is microgrids that can disconnect from the grid and offer grid resilience during an outage. While this technology is still finding its footing in the industry, states across the US are employing policy patterns and forms of instruments to support its deployment. This study includes a systemic review of the US by conducting a binary analysis of all 50 states (including Washington D.C, excluding other US territories) using seven variables. The results show four major policy approaches to microgrids: i) supporting microgrids through a definitive legislative activity leading to further policy action; ii) direct efforts from the public utilities commissions without a concrete legislative push; iii) initiatives from institutions other than the commissions; and lastly, iv) self-initiated community and private consumer efforts. The results help understand what policy instruments are being used in each of these patterns to support this niche technology that still faces regulatory challenges.

Furqan, Maham↗
DOE OSTI2020

Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience

Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.

42 ENGINEERING↗
DOE OSTI2025

Life-cycle cost-benefit analysis of a novel self-heating pavement made from coal-derived solid carbon

Winter weather events challenge the safety, efficiency, and sustainability of roadway networks, particularly road bridges vulnerable to climate impacts. Although effective, conventional de-icing methods incur high expenses and cause significant environmental contamination. To address these longstanding issues, a low-cost novel coal-derived carbon enabled smart pavement (CDC-SP) de-icing system was developed, aiming to alleviate traffic delays, severe corrosion, compromised safety, and environmental pollution. However, the economic performance of CDC-SP has not been quantified to guide decision-making. To this end, this paper presents a Life-Cycle Cost-Benefit Analysis (LCCBA) to assess the economic feasibility of the CDC-SP de-icing system for road bridges in five representative urban and rural areas across cool humid and cold humid climate zones. Given the variations and uncertainties of several factors that determine the costs and benefits, a sensitivity analysis was conducted to determine the system’s economic reliability. Additionally, a Monte Carlo simulation (MCS) was performed to quantify the impacts of important factors and identify the most beneficial scenarios. Furthermore, the CDC-SP de-icing systems have demonstrated substantial economic benefits and short payback periods, showing great applicability for rural and urban road bridges with different service levels across multiple climate zones as compared to conventional de-icing methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exploring acute weather resilience: Meeting resilience and renewable goals

We report the United States is affected by an average of almost seven severe weather events a year, often resulting in billions of dollars in physical and economic damages, a subset of which are related to grid outages. There is a need for power and energy system stakeholders to better understand and implement the strategies that help reduce net-economic and societal consequences associated with grid outages by improving the resilience of their systems. In addition, there are incentives to reduce emissions and meet climate goals, several pathways of which include resilient technologies. Including resilience constraints and metrics in energy system planning models may help inform the design of more resilient systems that are also more renewable and sustainable. This paper reviews qualitative definitions of resilience, quantitative approaches to resilience, recent examples of the inclusion of resilience in energy system models with respect to acute climatological threats, and the gaps in fully articulating resilience in current modeling tools. We then outline steps to effectively improve resilience considerations against such threats into energy sector modeling tools. Based on the findings, the authors propose a novel framework for energy system resilience assessment and future areas of research to bridge the current modeling gaps.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Antecedent conditions determine the biogeochemical response of coastal soils to seawater exposure

Coastal landscapes are increasingly exposed to seawater due to sea level rise and extreme weather events. The biogeochemical responses of these vulnerable ecosystems are poorly understood, limiting our ability to predict how their role in local and global biogeochemical cycles will shift under future conditions. Here we evaluate how antecedent conditions influence the biogeochemical response of soil to seawater inundation events based on a 42-day laboratory incubation experiment with soils collected from a natural salinity gradient across a coastal floodplain. We quantified influences of seawater inundation on intact soil cores through high-frequency carbon dioxide (CO 2 ) and methane (CH 4 ) gas fluxes measurements as well as ultrahigh resolution characterization of organic matter chemistry and metabolites. Mean CO 2 and CH 4 fluxes were higher after inundation compared to control cores for soils that had low in situ salinity. Soils with low in situ salinity also exhibited significant shifts in organic matter profiles after inundation, with surficial soils (0-7.5 cm) becoming more enriched in phenolic compounds, compared to deeper soils (7.5-15 cm). The number of biochemical transformations inferred from mass spectrometry increased significantly after inundation for soils with low in situ salinity, and a trend of decreasing thermodynamic favorability with increasing salinity was observed, suggesting an increase in microbial activity following inundation. Our results suggest that seawater inundation of low-salinity terrestrial environments can lead to increased microbial activity, preferential depletion of more thermodynamically favorable compounds, and increased soil carbon release. Finally, we conclude that the biogeochemical impacts of future seawater exposure will be modulated by antecedent conditions associated with landscape position within coastal watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2024

Capacity contributions of Southern Oregon offshore wind to the Pacific Northwest and California

Variable renewable energy generation poses unique capacity challenges, which increasingly depend on weather events at varying timescales. Facilitated by transmission planning, geographic and technological diversity of the generation fleet may provide a mitigation to capacity shortfalls. In this work, offshore wind (OSW) energy is sited in the areas off the West Coast between Coos Bay, Oregon, and Crescent City, California. Three generation and transmission scenarios are modeled within the Western Interconnection: (i) 3.4 gigawatts (GW) of installed OSW capacity connected to Southern Oregon through a High Voltage Alternating Current (HVAC) Radial Topology in 2030; (ii) 12.9 GW of installed OSW capacity connected to Washington, Oregon, and California through a High Voltage Direct Current (HVDC) Radial Topology post-2030, and (iii) the same 12.9 GW connected to the same locations through a Multi-terminal DC (MTDC) Backbone Topology post-2030. Zonal dispatch simulations assuming coincident wind, solar, and hydropower production and loads over 18 meteorological years, accounting for temperature-dependent equipment derating and forced outages, serve as inputs to the Associated System Capacity Contribution (ASCC) methodology. The capacity credit is 33%, 25% and 34% for the 2030 HVAC Radial Topology, 2030+ HVDC Radial Topology, and 2030+ MTDC Backbone Topology, respectively. Transmission design is shown to mitigate the typical erosion of marginal capacity contribution as more OSW is developed, underscoring the opportunity for grid modernization while decarbonizing the generation mix.

17 WIND ENERGY↗
DOE OSTI2024

Reactive Carbon Capture: Cooperative and Bifunctional Adsorbent-Catalyst Materials and Process Integration for a New Carbon Economy

To say the least, releasing CO 2 into the atmosphere is reaping undue environmental consequences given the ever-present increase in severe global weather events over the past five years. However, it can be argued that–at least in the confines of current technological capabilities–the atmospheric release of CO 2 is somewhat unavoidable given that even shifting toward clean energy sources–such as solar, nuclear, wind, battery, or H 2 power–incurs an initial carbon requirement by way of manufacturing the very production abilities through which “clean” energy is generated. Even years from now, experts agree that energy production will be diversified and–as the global population continues to drive the growth of global energy consumption–thermal power derived from carbon combustion is likely to remain one intrinsic energetic source, of which CO 2 will always be a byproduct. In this context, it is the responsibility of the scientific community to devise improved pathways of carbon management such that (i) the consequences of combustion on the global environment are reduced and (ii) carbon fuels can be leveraged in a sustainable fashion. In this Account, we discuss a pivotal perspective shift on CO 2 emissions derived from a considerable breakthrough in material science from our work on shape engineering of nanoporous adsorbents and catalysts. This account details the development of materials which no longer vilify CO 2 emissions as a valueless combustion byproduct, instead providing a path for them to become a potential feedstock. In more specific terms, this work details the development of structured, cooperative “bifunctional” materials (BFMs) comprised of (i) a high-temperature adsorbent and (ii) a heterogeneous catalyst that enable single-bed CO 2 capture and utilization in oxidative ethane dehydrogenation (ODHE), oxidative propane dehydrogenation (ODHP), and dry methane reforming (DMR) processes. This Account begins with the conceptual development of the BFMs in the powdered state, followed by detailing the first-ever reports of structuring the materials into facile honeycomb contactors by 3D printing. The Account then summarizes the impressive performance of the 3D-printed BFMs, specifically focusing on how their catalysts (metal oxides and perovskites) influence their reactive CO 2 capture performances in ODHE, ODHP, and DMR processes. Such promise of CO 2 -as-fuel offers a glimpse into the future of a diversified energy economy, in which CO 2 /fuel looping can play an important role. A major factor in achieving this future is, of course, developing an appropriately active catalyst; an account of whose first breakthroughs in material science are detailed herein.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.

Rutz, Jonathan J.↗
DOE OSTI2021

Exploring Sources of Surface Bias in HRRR Using New York State Mesonet

In recent years, there has been increasing demand for applications of short-term forecasting of renewable energy potential and assessments of the likelihood of extreme weather events using the High-Resolution Rapid Refresh (HRRR) model. Examining the biases in the newest version of HRRR is necessary to promote further model development. Using data from one of the most comprehensive and dense monitoring networks, New York State Mesonet (NYSM), we evaluate the HRRR version 3 meteorological fields for an entire year. In this work, the land-atmosphere-cloud coupling system is evaluated as an integrated whole. We investigate the physical processes influencing the soil hydrological balance and the thermodynamic interactions, from surface fluxes up to the level of boundary layer convection from both temporal (seasonal and diurnal) and spatial perspectives. Results show that the model 2 m temperature and humidity biases are seasonally dependent, with warm and dry bias present during the warm season, and an extreme nocturnal cold bias in winter. The summer warm bias includes both a land-surface-induced bias and a cloud-induced bias. Inaccurate representation of energy partition and soil hydrological process across different land use types as well as a hydrological bias in describing spring snowmelt are identified as the main source of the land-surface-induced bias. A feedback loop linking cloud presence, flux changes, and temperature contributes to the cloud-induced bias. The positive solar radiation bias increases from clear sky to overcast sky conditions. The most significant bias occurs during overcast and thick cloud conditions associated with frontal passage and thunderstorms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Convection-Permitting Simulations With the E3SM Global Atmosphere Model

This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40-day prescribed-sea-surface-temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be ~ 6x the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long-standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models With 3D Diffusion Models

Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low-cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low-cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio-temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

Abadi, Azar M. [University of Alabama, Birmingham,↗