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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Climate impacts in scenarios: time to close the loop?

Reaching a full understanding of the consequences of climate change for society and ecosystems, and the ensuing needs for adaptation, requires a consideration of the interactions between human and Earth systems. Currently, however, climate research largely separates the influence of society on climate from the influence of climate on society; that is, it doesn’t “close the loop.” A primary example of this approach is the generation and use of earth system model (ESM) simulations in the climate change research community. Large-scale socio-economic models, known as integrated assessment models (IAMs), are used to project emissions and land use change which serve as input to ESMs. ESM projections then serve as input to models of impacts on society and ecosystems. But, according to this modeling chain, those impacts do not affect the emissions and land use that drove the ESMs in the first place. Previous work has not drawn firm conclusions on whether this feedback would be large enough to warrant explicitly accounting for it. Two prominent possibilities, however, are that emissions and land use scenarios representing the high and low ends of the plausible range of future climate change are both too extreme. The high-end scenario may miss damaging impacts that would reduce economic activity, and therefore emissions, while the low-end scenario may ignore climate feedbacks that would make large-scale land-based carbon removal ineffective and therefore would hamper mitigation at the level assumed by the scenario. In this piece, we identify the opportunities and challenges that implementing such feedback loops would face. We argue that recent developments in climate impact research, human system modeling and ESM emulation make the time ripe to use IAMs in a structured model intercomparison exercise. Model intercomparison projects have benefitted the climate modeling community for decade snow, and more recently have also benefitted the impact modeling community. An IAM intercomparison focused on integrating impacts could make large strides in testing the implications of these feedbacks and assessing whether closing the loop would fundamentally change our outlook on future climate changes and their consequences.

Tebaldi, Claudia↗
DOE OSTI2024

Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models

Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Snyder, Abigail C.↗
DOE OSTI2025

Reactor System Facility Modification to Detect Compromised Human Machine Interfaces

This study focuses on a multi-layered Industrial Control System (ICS)/Operational Technology (OT) security architecture to aid in the discovery and mitigation of compromised Human Machine Interface (HMI)/Instrumentation & Control (I&C) based systems for modifying a prototypical reactor condition test facility called the Flowing Autoclave System (FAS) at Idaho National Laboratory (INL). This is achieved through a three-layered combination of network security solutions, hash-based algorithms, and blockchain technologies. Hash algorithms are mathematical functions used to generate a predetermined set of fixed-length values. They are widely used in computer security to verify the integrity of system information and data, both on a local network and the wider internet. Even small amounts of unauthorized system modification will cause the hash algorithm to output a set of characters that deviate significantly from its original value. Assisting secure hash functions, blockchain technology is a secure and distributed technology used to provide an immutable set of records replicated on all devices within a decentralized network. Blockchain offers a cost-effective solution to detect system compromise by providing a traceable breadcrumb trail of all network activity and data modification happening on a system. If both are used in conjunction with network monitoring tools, the integration of this three-pronged approach can become an asset in detecting suspected system compromises before any real damage can occur.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

MSD CoP Webinar: "Generative agents: A new frontier for representing human actors and their behavior in MSD models"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Talk #1: Behavioral Generative Agents for Energy Operations Presenter: Dr. Cong Chen (Thayer School of Engineering, Dartmouth College) Abstract: Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This talk introduces a novel approach leveraging generative agents--artificial agents powered by large language models--to realistically simulate customer decision-making in dynamic energy operations. Talk #2: Simulating multiple human perspectives in socio-ecological systems using large language models Presenter: Dr. Yongchao Zeng (Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) of the Karlsruhe Institute of Technology in Germany) Abstract: Understanding socio-ecological systems requires insights from diverse stakeholder perspectives. This talk describes a novel simulation system called HoPeS (Human-oriented Perspective Shifting). HoPeS enables model users to not only explore simulated socio-ecological systems (SESs) from a third-person observer's perspective but also take any of the simulated stakeholder roles, like playing an RPG game. By shifting multiple perspectives, model users can reflect and integrate the situated knowledge learned through the participatory simulation, approximating a more holistic and less biased understanding of SESs. Moderators: Jim Yoon (MSD CoP Human Systems Modeling Working Group Co-Chair); Stefano Galelli (MSD CoP Using AI to Enhance MSD Research Working Group Co-Chair); Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: November 13th, 2025 from 12-1 PM EST.

Artificial Intelligence↗
DOE OSTI2020

Quantitative Analysis of the Effects of Natural and Human Factors on a Hydrological System in Zhangweinan Canal Basin

Drought is a water deficit state caused by large-scale climate change that cannot be avoided by a water resource management system. Water scarcity refers to the unsustainable utilization of water resources over a long time, which is the result of water management policies. However, water deficits caused by drought and water scarcity often occur simultaneously and are indistinguishable. This study proposes a model-based simulation framework that can quantitatively distinguish natural factors (drought) from human factors (water scarcity) in a hydrological system. The simulation was applied to the Zhangweinan Canal Basin, based on the runoff sequences from 1950 to 2004. The results show that the runoff curve number, soil depth, soil available water, soil evaporation compensation coefficient, base runoff α coefficient and the maximum canopy interception have the highest sensitivity to runoff, and that the calibrated and validated SWAT model can effectively simulate the runoff process in the Zhangweinan Canal Basin and similar areas. Abrupt changes in human activities in 1975 and water scarcity led to the disappearance of the summer peak runoff period in both wet and dry years. Human factors are the main reason for the change in the hydrological system in the study area; the runoff loss caused by human factors is four times that caused by natural factors according to the proposed variable threshold. This study proposes a model-based simulation framework that can help water resource managers to distinguish the effects of drought and water scarcity in water-stressed areas and adjust management accordingly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Advancing Diagnostic Model Evaluation to Better Understand Water Shortage Mechanisms in Institutionally Complex River Basins

Abstract Water resources systems models enable valuable inferences on consequential system stressors by representing both the geophysical processes determining the movement of water and the human elements distributing it to its various competing uses. This study contributes a diagnostic evaluation framework that pairs exploratory modeling with global sensitivity analysis to enhance our ability to make inferences on water scarcity vulnerabilities in institutionally complex river basins. Diagnostic evaluation of models representing institutionally complex river basins with many stakeholders poses significant challenges. First, it needs to exploit a large and diverse suite of simulations to capture important human‐natural system interactions as well as institutionally aware behavioral mechanisms. Second, it needs to have performance metrics that are consequential and draw on decision‐relevant model outputs that adequately capture the multisector concerns that emerge from diverse basin stakeholders. We demonstrate the proposed model diagnostic framework by evaluating how potential interactions between changing hydrologic conditions and human demands influence the frequencies and durations of water shortages of varying magnitudes experienced by hundreds of users in a subbasin of the Colorado River. We show that the dominant factors shaping these effects vary both across users and, for an individual user, across percentiles of shortage magnitude. These differences hold even for users sharing diversion locations, demand levels or water right seniority. Our findings underline the importance of detailed institutional representation for such basins, as institutions strongly shape how dominant factors of stakeholder vulnerabilities propagate through the complex network of users.

Hadjimichael, Antonia↗
DOE OSTI2023

Human Factors for Advanced Reactors

Existing light water reactors in the U.S. are primarily large baseload electricity generating facilities. The concept of operations for these plants remains largely unchanged since the advent of commercial nuclear power—the main control room serves as the hub of plant activities and is staffed with multiple licensed operators who work in tandem under the shift supervisor, and staff such as field workers support the control room remotely. While newer plants have brought the advent of digital human-machine interfaces to replace earlier analog and mechanical instrumentation and controls, much of the control process remains unchanged and manual. It is simply a newer version of legacy concepts. Advanced reactors potentially bring considerable changes to the size, fuel type, automation, and staffing of nuclear power plants, necessitating a fundamental shift not just from analog to digital, but further from human to automation, from onsite to remote, from control to monitoring, and from many to few operators. Despite this multitude of parallel evolutions in reactor designs, many of the vendors developing the next generation of reactors represent smaller research and development enterprises. It is therefore not feasible to address all aspects of plant design at the same time. In particular, the competing design aspects of new reactors present a significant challenge to the development of robust and human factored systems at the plant. As vendors develop new reactor designs, much of the early focus is naturally on the fuel and reactor system technology. Looming behind these early advances is the daunting prospect of first-of-a-kind control concepts that have not yet been developed or validated. A failure to address the human element of reactor design early will lead to missed opportunities. The quickest development process is the replication of existing concepts of operations at legacy plants, even when such systems were long ago surpassed by better human-machine technologies outside the nuclear industry. Conversely, attempting to undertake novel concepts of operations late in the design life cycle of a plant could result in protracted development efforts and delays in licensing and deployment. This does not have to happen, and it is imperative that human factors be considered now, early in the design of new reactors.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Seeing the forest for the trees: implementing dynamic representation of forest management and forest carbon in a long-term global multisector model

Abstract Studies have found that understanding forest management is critical in understanding the interaction between the carbon cycle and the integrated human-Earth system. This makes effectively representing forest management decisions such as planting and harvesting important. Here, we implement a novel dynamic forest harvest model in a global state of the art multi-sector dynamics model, namely the Global Change Analysis Model (GCAM). We implement an approach that explicitly tracks forest age and generates rotation ages for forest harvest that are responsive to changes in wood prices, changes in forest age and regional preferences for forest rotation. Furthermore, the forest sector in GCAM competes for investment with other land use types in the future years based on expected profit. Our baseline scenario results indicate that with the new forest harvest model, the current global wood product demand in GCAM can be met with minimal loss of old growth forest through the age-based harvest decisions. We find that economic pressure for deforestation and consequent loss of forest carbon is a bigger driver of global forest change than wood harvests, especially in developing regions. Under alternative scenarios where an economic value is placed on carbon across the terrestrial and energy systems, while there is an increase in forest plantations, there can be corresponding decreases in forest cover in some regions as forest land competes with land for bio-energy crops. When the carbon in forests is assigned a price, we find that the average rotation age for wood harvests can be reduced across regions to harvest forests in a more carbon efficient manner.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Limited increases in Arctic offshore oil and gas production with climate change and the implications for energy markets

Climate change impacts on sea ice thickness is opening access to offshore Arctic resources. The degree to which these resources are exploited will depend on sea-ice conditions, technology costs, international energy markets, and the regulatory environment. We use an integrated human-Earth system model, GCAM, to explore the effects of spatial–temporal patterns of sea-ice loss under climate change on future Arctic offshore oil and gas extraction, considering interactions with global energy markets and emission reduction scenarios. We find that under SSP5, a “fossil-fueled development” scenario, the effects of sea-ice loss are larger for Arctic offshore oil production than gas. Under SSP5, future extraction of Arctic offshore oil and gas through 2100 adds roughly 0.8–2.6 EJ/year to oil and gas markets but does not have large impacts on global oil and gas markets. Surprisingly, a low-carbon scenario results in greater Arctic offshore oil production to offset the more emissions-intensive unconventional oil production.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Leveraging Prior Concept Learning Improves Generalization From Few Examples in Computational Models of Human Object Recognition

Humans quickly and accurately learn new visual concepts from sparse data, sometimes just a single example. The impressive performance of artificial neural networks which hierarchically pool afferents across scales and positions suggests that the hierarchical organization of the human visual system is critical to its accuracy. These approaches, however, require magnitudes of order more examples than human learners. We used a benchmark deep learning model to show that the hierarchy can also be leveraged to vastly improve the speed of learning. We specifically show how previously learned but broadly tuned conceptual representations can be used to learn visual concepts from as few as two positive examples; reusing visual representations from earlier in the visual hierarchy, as in prior approaches, requires significantly more examples to perform comparably. These results suggest techniques for learning even more efficiently and provide a biologically plausible way to learn new visual concepts from few examples.

Rule, Joshua S.↗
DOE OSTI2021

Dynamic Human-in-the-Loop Simulated Nuclear Power Plant Thermal Dispatch System Demonstration and Evaluation Study

An Idaho National Laboratory research team performed a human-in-the-loop study with two formerly licensed retired operators to evaluate thermal power dispatch operations supported by the modified GSE Systems GPWR plant simulator. Virtual representations of the analog control panels were presented on touch-screen bays configured to mimic the control room layout in the newly renovated Human Systems Simulation Laboratory. The operators performed 15 scenarios covering normal evolutions to transition the plant from full turbine operation to joint turbine and thermal power dispatch operations in addition to transient response scenarios induced with simulated faults to evaluate the impact of thermal power dispatch system on operator and plant responses. A prototype human-system interface (HSI) was developed and displayed in tandem with the virtual analog panels to support the operators executing the procedurally drive evolutions and transient responses. An interdisciplinary team of operations experts, nuclear engineers, and human factors experts observed the operators performing the scenarios to evaluate the operations. Only a preliminary analysis of the results has been performed. The full analysis will be shared in a milestone report scheduled for release at the end of September; however, some high-level conclusions were readily available. Two high level findings for the system design were captured in the study. The manual control supported by the HSI to transition from standard operations to thermal power dispatch operation imposed a considerable amount of workload on the operators due to tedious manual valve manipulations and system monitoring required to verify their intended effect. An additional operator would be required in the control room to support the daily evolution. Automatic control for the transition was deemed a requirement for plant adoption without imposing additional staffing costs. The second finding was the necessity for an automatic thermal power dispatch system trip isolation function linked to a turbine and reactor trip signal. The operators completed scenarios with automatic isolation functionality and manually required actuation of the thermal power dispatch system. The operator response was sufficiently slower in the manual trip condition, such that operators were unable to manually actuate key post trip safety functions an indicate a degraded control capability that should be avoided. With an automatic trip signal little to no impact of the thermal power dispatch system was identified on the primary plant response and therefore the system could be readily and safely adopted. Together these two findings represent the need to support the adoption of thermal power dispatch capability into existing operations by leveraging automation to augment any additional operator tasking required to control and monitor an additional system beyond existing operations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

Understanding the interaction between humans and the Earth system is a computationally daunting task, with many possible approaches depending on resources available and questions of interest. For example, state-of-the-art impact models require decade-long time series of relatively high frequency, spatially resolved and often multiple variables representing climatic impact-drivers (Ruane et al., 2022). Most commonly these are derived from the outputs of detailed, computationally expensive Earth System Models (ESMs) run according to a standard, limited set of future scenarios, the latest being the SSP-RCPs run under CMIP6/ScenarioMIP (Eyring et al., 2016; O’Neill et al., 2016). At the time of writing, O’Neill et al. (2016) has been cited more than 1750 times and Eyring et al. (2016) more than 5000 times, highlighting the broad, general applications of this data. Often, however, impact modeling seeks to explore new scenarios that were not part of the ScenarioMIP protocol, and/or needs a larger set of initial condition ensemble members than are typically available to quantify the effects of ESM internal variability. In addition, the recognition that the human and Earth systems are fundamentally intertwined, and may feature potentially significant feedback loops, is making integrated, simultaneous modeling of the coupled human-Earth system increasingly necessary, if computationally challenging with most existing tools (Thornton et al., 2017). For impact modelers, climate model emulators can be the answer to meet both the needs of: 1) creating realizations for novel scenarios and 2) achieving a simplified, computationally tractable representation of ESM behavior in a coupled human-Earth system modeling framework. We proposed a new, comprehensive approach to such emulation of gridded, multivariate ESM outputs for novel scenarios without the computational cost of a full ESM, STITCHES (Tebaldi et al., 2022). The approach outlined in Tebaldi et al. (2022) should be extensible to future CMIP eras, although the STITCHES software at present is strictly focused on CMIP6/ScenarioMIP data hosted on Pangeo (https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/). The corresponding STITCHES Python package uses existing archives of ESMs’ scenario experiments from CMIP6/ScenarioMIP to construct gridded, multivariate realizations of new scenarios provided by reduced complexity climate models (Hartin et al., 2015; Meinshausen et al., 2011; Smith et al., 2018), or to enrich existing initial condition ensembles. Its output provides the same characteristics as the emulated ESM output: multivariate (spanning potentially all variables that the ESM has saved), spatially resolved (down to the native grid of the ESM), and preserving the same high frequency as the original data. A new realization of multiple variables can be generated on the order of minutes with STITCHES, rather than the hours or sometimes days that ESMs require.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bridging the Gap Between Modern UX Design and Particle Accelerator Control Room Interfaces

Accelerator control systems often represent relatively complex and safety-sensitive human-machine interfaces within process control industries. These systems are technically robust and reflect the cumulative integration of solutions built and adapted across decades. One of the regular, unfortunate casualties of provisional accelerator control system updates is their human-system interfaces (HSIs) which often lag behind modern usability and design standards. An additional challenge is that although there is a multitude of established human factors (HF), and user experience (UX) principles for everyday digital applications, there are very few (if any) established principles for complex and safety-critical applications for an accelerator. This paper argues for the importance of established HF and UX principles (herein referred to as human-centered design principles) into the development of accelerator HSIs, emphasizing the need for clarity, consistency, responsiveness, and cognitive accessibility. Drawing from HF/UX best practices and human-centered design, this paper discusses how these approaches can enhance operator performance, reduce human error, and improve accelerator personnel collaboration. Case studies from Accelerator Control Operations Research Network (ACORN) at Fermilab are explored to demonstrate how interfaces built with human-centered design principles can scale with system complexity while remaining intuitive and efficient for diverse user roles including operators, machine experts, and engineers. By bridging the gap between traditional control system design and modern human-centered design methods, this paper provides a roadmap for evolving accelerator HSIs into more usable, maintainable, and effective tools.

Hill, Rachael [Idaho Natl. Lab.]↗
DOE OSTI2021

The Implications of Global Change for the Co-Evolution of Argentina's Integrated Energy-Water-Land Systems

This study seeks to understand how Argentina’s energy, water, and land (EWL) systems will co-evolve under a representative array of human and earth system influences, including socioeconomic change, climate change, and climate policy. To capture Argentina’s sub-national EWL dynamics in the context of global change, we couple the Global Change Analysis Model with a suite of consistent, gridded sectoral downscaling models to explore multiple stakeholder-engaged scenarios. Across scenarios, Argentina has the economic opportunity to use its vast land resources to satisfy growing domestic and international demand for crops, such as oil (e.g., soy) and biomass. The human (rather than earth) system produces the most dominant changes in mid-century EWL resource use. A Reference scenario characterized by modest socioeconomic growth projects a 40% increase in Argentina’s agricultural production by 2050 (relative to 2020) by using 50,000 km 2 of additional cropland and 40% more water. A Climate Policy scenario designed to achieve net-zero carbon emissions globally shortly after mid-century projects that Argentina could use 100,000 km 2 of additional land (and 65% more water) to grow biomass and other crops. The burden of navigating these national opportunities and challenges could fall disproportionately on a subset of Argentina’s river basins. The Colorado and Negro basins could experience moderate-to-severe water scarcity as they simultaneously navigate substantial irrigated crop demand growth and climate-induced declines in natural water availability. Argentina serves as a generalizable testbed to demonstrate that multi-scale EWL planning challenges can be identified and managed more effectively via integrated analysis of coupled human-earth systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information

Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf’s ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

GCAM v4.3 SSP-RCP-GCM Output Products

Supplemental dataset to: Graham N.T., M.I. Hejazi, M. Chen, E. Davies, J.A. Edmonds, S.H. Kim, and S. Turner, et al. 2020. "Humans drive future water scarcity changes across all Shared Socioeconomic Pathways." Environmental Research Letters 15, no. 1:Article No. 014007. PNNL-SA-151297. doi:10.1088/1748-9326/ab639b This analysis makes deterministic classifications of how, when water resources are limited and constrained, the human and climate systems interact. The relative effects of both systems on water scarcity are quantified at global and basin scales across 15 global futures that include five different socioeconomic conditions (the Shared Socioeconomic Pathways, SSPs) and four different climatic conditions (the Representative Concentration Pathways, RCPs). These 15 scenarios are used to first analyze a 'Human Alone' component by isolating the human impact on future scarcity. This is accomplished by holding all climate variables to their 2005 levels while altering socioeconomic growth and technological change. General circulation model (GCM) derived climate impacts for five models are applied to establish 75 'Human and Climate' scenarios which allows for the quantification of climate impacts. Climatic impacts to water supply, agricultural productivity and change, hydropower availability, and building energy demands are applied from 5 different bias-corrected GCMs to make a suite of 75 climate runs. By subtracting the human derived impacts from the 'Human Alone' scenarios, from the 'Human and Climate' scenarios the climate impact is isolated. First, the drivers of future water scarcity are evaluated by isolating the impacts that both humans and climate have while accounting for feedbacks between humans, energy, and land. Secondly, the simultaneous impacts that human and climate systems have on water scarcity are analyzed by determining whether each system is increasing or decreasing scarcity in all global water basins. Below we describe the GCAM model, the scenario components, climate derived impacts, and the calculations of scarcity changes and attribution. This study uses the global change assessment model (GCAM), with inclusions of water constraints to both renewable and nonrenewable sources of water, to investigate the relative contributions of climate and human systems on water scarcity regionally and globally under a wide range of scenarios. GCAM links socioeconomics, the energy system, land-use change, climate, and the water sector. GCAM is a market-equilibrium model that allows for prices to be adjusted within each time step to ensure that the supply and demand of goods and services remains equilibrated at each time step allowing for simultaneous market clearing across sectors. This study accounts for a limited supply of water by employing cost resource curves across all 235 basins that follow a logit formulation to determine the share of each water source (renewable surface water, nonrenewable groundwater, and desalinated water) needed to meet the water demands within all basins. As depletion of various water sources increases the extraction price increases, which leads to compounding price increases on the goods and services that require higher-priced water sources.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Cellular response of keratinocytes to the entry and accumulation of nanoplastic particles

Plastic accumulation in the environment is rapidly increasing, and nanoplastics (NP), byproducts of environmental weathering of bulk plastic waste, pose a significant public health risk. Particles may enter the human body through many possible routes such as ingestion, inhalation, and skin absorption. However, studies on NP penetration and accumulation in human skin are limited. Loss or reduction of the keratinized skin barrier may enhance the skin penetration of NPs. The present study investigated the entry of NPs into a human skin system modeling skin with compromised barrier functions and cellular responses to the intracellular accumulations of NPs. Two in vitro models were employed to simulate human skin lacking keratinized barriers. The first model was an ex vivo human skin culture with the keratinized dermal layer (stratum corneum) removed. The second model was a 3D keratinocyte/dermal fibroblast cell co-culture model with stratified keratinocytes on the top and a monolayer of skin fibroblast cells co-cultured at the bottom. The penetration and accumulation of the NPs in different cell types were observed using fluorescent microscopy, confocal microscopy, and cryogenic electron microscopy (cryo-EM). The cellular responses of keratinocytes and dermal fibroblast cells to stress induced by NPs stress were measured. The genetic regulatory pathway of keratinocytes to the intracellular NPs was identified using transcript analyses and KEGG pathway analysis. The cellular uptake of NPs by skin cells was confirmed by imaging analyses. Transepidermal transport and penetration of NPs through the skin epidermis were observed. According to the gene expression and pathway analyses, an IL-17 signaling pathway was identified as the trigger for cellular responses to internal NP accumulation in the keratinocytes. The transepidermal NPs were also found in co-cultured dermal fibroblast cells and resulted in a large-scale transition from fibroblast cells to myofibroblast cells with enhanced production of α-smooth muscle actin and pro-Collagen Ia. The upregulation of inflammatory factors and cell activation may result in skin inflammation and ultimately trigger immune responses.

36 MATERIALS SCIENCE↗