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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning

Abstract The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km 2 ) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km 2 . Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Song, Yalan [Civil and Environmental Engineering T↗
DOE OSTI2025

Stochastic 3D reconstruction of cracked polycrystalline NMC particles using 2D SEM data

Li-ion battery performance is strongly influenced by the 3D microstructure of its cathode particles. Cracks within these particles develop during calendaring and cycling, reducing connectivity but increasing reactive surface, making their impact on battery performance complex. Understanding these contradictory effects requires a quantitative link between particle morphology and battery performance. However, informative 3D imaging techniques are time-consuming, costly and rarely available, such that analyses often have to rely on 2D image data. This paper presents a novel stereological approach for generating virtual 3D cathode particles exhibiting crack networks that are statistically equivalent to those observed in 2D sections of experimentally measured particles. Consequently, 2D image data suffices for deriving a full 3D characterization of cracked cathodes particles. Such virtually generated 3D particles could serve as geometry input for spatially resolved electro-chemo-mechanical simulations to enhance our understanding of structure-property relationships of cathodes in Li-ion batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling Electric Vehicle Charging Load Using Origin-Destination Data

The accelerating adoption of electric vehicles (EVs) poses challenges to the power grid, necessitating precise representation of mobility patterns for effective infrastructure upgrades. Traditional simulation-based charging demand estimation faces limitations in generating trip chains reflective of actual travel patterns without complex network modeling. Hence, an innovative agent-based trip chain generation model is introduced to overcome these challenges. Drawing from the National Household Travel Survey (NHTS) and the NextGen NHTS origin-destination add-on data for Clarke County, Georgia, this study proposes a simulation method capturing both temporal and spatial mobility patterns without relying on extensive network topology data. The resulting trip chains predict EV charging load at the Census Block Group level, validated with a 1.03 correlation to actual trip counts, affirming their reflective accuracy. Two charging scenarios, residential-only and charging-everywhere, reveal distinct demand profiles. The charging-everywhere scenario aligns closely with the trip profile, while the residential-only scenario exhibits an afternoon peak slightly surpassing the former. This study contributes a data-driven charging demand estimation methodology, offering critical insights for grid resiliency planning amid the evolving landscape of EV adoption.

Pan, Melrose↗
DOE OSTI2024

Heat release surrogates for NH 3 /H 2 /N 2 –air premixed flames

The present study investigates the performance of NH, NH 2 , O-atom, and H-atom as heat release rate (HRR) surrogates for NH 3 /H 2 /N 2 –air premixed flame through simulations. The simulations are conducted across different pressures, reactant inlet temperatures, fuel blend compositions, and equivalence ratios. Cantera is used to simulate one-dimensional (1D) freely propagating flames to investigate the spatial correlations of the species with the HRR. PeleLMeX, a low-Mach direct numerical simulation (DNS) code with Adaptive Mesh Refinement (AMR), is used to simulate two-dimensional (2D) flame-vortex interactions to investigate the temporal correlations including stretch effects. Three different mechanisms (Jiang et al. 2020; Glarborg et al. 2018; Okafor et al. 2018) were considered in the 1D flame simulations, whereas only the Jiang mechanism was considered in the flame-vortex simulations. The HRR surrogate performance for the 2D flames is evaluated at two different locations: (1) the centerline and (2) the flame cusp. The cusp is defined as the region in the flame front with the greatest curvature and the centerline encounters the highest tangential strain rate. The 1D flame results suggest that, although there is not uniformly good spatial correlation for HRR across all flame conditions, NH is the best overall as a HRR surrogate for laminar flames. The 2D flame results, however, suggest that O-atom and H-atom have satisfactory temporal correlations at different conditions—the former for rich flames, the latter for high-pressure flames. Furthermore, these simulations provide guidance to experimental measurements of surrogate HRR markers in unsteady multi-dimensional flames using laser diagnostics to detect species such as NH, O-atom, and H-atom.

Ammonia↗
DOE OSTI2024

A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions

New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO 3 , CaZrN 2 , and YMnO 3 found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids in silico .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

LES and RANS Spray Combustion Analysis of OME 3-5 and n-Dodecane

Clean-burning oxygenated and synthetic fuels derived from renewable power, so-called e-fuels, are a promising pathway to decarbonize compression–ignition engines. Polyoxymethylene dimethyl ethers (PODEs or OMEs) are one candidate of such fuels with good prospects. Their lack of carbon-to-carbon bonds and high concentration of chemically bound oxygen effectively negate the emergence of polycyclic aromatic hydrocarbons (PAHs) and even their precursors like acetylene (C 2 H 2 ), enabling soot-free combustion without the soot-NO x trade-off common for diesel engines. The differences in the spray combustion process for OMEs and diesel-like reference fuels like n-dodecane and their potential implications on engine applications include discrepancies in the observed ignition delay, the stabilized flame lift-off location, and significant deviations in high-temperature flame morphology. For CFD simulations, the accurate modeling and prediction of these differences between OMEs and n-dodecane proved challenging. This study investigates the spray combustion process of an OME 3 − 5 mixture and n-dodecane with advanced optical diagnostics, Reynolds-Averaged Navier–Stokes (RANS), and Large-Eddy Simulations (LESs) within a constant-volume vessel. Cool-flame and high-temperature combustion were measured simultaneously via high-speed (50 kHz) imaging with formaldehyde (CH 2 O) planar laser-induced fluorescence (PLIF) representing the former and line-of-sight OH* chemiluminescence the latter. Both RANS and LES simulations accurately describe the cool-flame development process with the formation of CH 2 O. However, CH 2 O consumption and the onset of high-temperature reactions, signaled by the rise of OH* levels, show significant deviations between RANS, LES, and experiments as well as between n-dodecane and OME. A focus is set on the quality of the simulated results compared to the experimentally observed spatial distribution of OH*, especially in OME fuel-rich regions. The influence of the turbulence modeling is investigated for the two distinct ambient temperatures of 900 K and 1200 K within the Engine Combustion Network Spray A setup. The capabilities and limitations of the RANS simulations are demonstrated with the initial cool-flame propagation and periodic oscillations of CH 2 O formation/consumption during the quasi-steady combustion period captured by the LES.

CFD↗
DOE OSTI2024

A new synthetic correlation electron cyclotron emission diagnostic for validating nonlinear gyrokinetic simulations of electron temperature turbulence

To validate nonlinear gyrokinetic simulations of electron temperature turbulence, the experimental correlation electron cyclotron emission (CECE) measurements are to be compared using a synthetic CECE diagnostic, which generates modeled CECE measurement quantities by implementing realistic measurement parameters (e.g., spatial and wavenumber resolutions, radial location, etc.) to nonlinear gyrokinetic simulations. In this work, we calculate the radial and vertical spatial and wavenumber transfer functions, which are defined by the electron cyclotron emission emissivity radial profile and vertical probing antenna pattern, respectively. These transfer functions are applied to nonlinear gyrokinetic simulations of electron temperature turbulence using the continuum gyrokinetic code. A simultaneous comparison of the experimental electron temperature turbulence power spectrum and root-mean-square (RMS) level, as well as the radial correlation length with the new synthetic CECE diagnostic at a core location ρ ∼ 0.75 in an L-mode DIII-D tokamak plasma, is presented. Here, the preliminary result shows that the synthetic CECE output underestimates the RMS level by ∼42% and overestimates the radial correlation length by ∼40%.

Cyclotron radiation↗
DOE OSTI2025

Improved honeycomb and hyperhoneycomb lattice Hamiltonians for quantum simulations of non-Abelian gauge theories

Improved Kogut-Susskind Hamiltonians for quantum simulations of non-Abelian Yang-Mills gauge theories are developed for honeycomb (2+1⁢D) and hyperhoneycomb (3+1⁢D) spatial tessellations. This is motivated by the desire to identify lattices for quantum simulations that involve only 3-link vertices among the gauge field group spaces in order to reduce the complexity in applications of the plaquette operator. For the honeycomb lattice, we derive a classically 𝒪⁡(𝑏 2 )-improved Hamiltonian, with 𝑏 being the lattice spacing. Tadpole improvement via the mean-field value of the plaquette operator is used to provide the corresponding quantum improvements. We have identified the (nonchiral) hyperhoneycomb as a candidate spatial tessellation for 3+1⁢D quantum simulations of gauge theories, and determined the associated 𝒪⁡(𝑏)-improved Hamiltonian.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

An atomistic survey of shear coupling in asymmetric tilt grain boundaries and interpretation using the disconnections framework

Grain Boundaries (GB) play an important role in determining the behavior of polycrystalline materials. While the mechanisms of motion and associated shear response for symmetric tilt grain boundaries (STGBs) are well studied, the same is not true for asymmetric tilt grain boundaries (ATGBs) despite their greater prevalence in polycrystals. Here, this study aims to investigate the shear response of a large collection of asymmetric tilt grain boundaries (ATGBs) using molecular dynamics (MD) simulations and interpret the data using a discrete disconnections model that works remarkably well for STGBs. MD simulations of shear-driven ATGBs show that the plastic shear (shear coupling factor) in the region swept by a GB exhibits a complex dependence on the inclination angle, and this dependence changes with the misorientation of the GB. In addition, the shear response was observed to be highly sensitive to the applied shear rate and temperature. Recognizing the spatial and temporal scale limitations of MD simulations, we extended the discrete disconnections mesoscale model of Khateret al. (2012) to calculate the nucleation barriers of disconnection modes and predict the effective shear coupling of an ATGB. We observed that the mesoscale model’s predictions of the shear coupling factor of ATGBs do not agree with those observed in MD simulations. Finally, we examine the hypotheses of our mesoscale model that contribute to disagreements between MD simulations and the mesoscale model and propose improvements to the mesoscale model for future work.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A robust approach to Gaussian process implementation

Abstract. Gaussian process (GP) regression is a flexible modeling technique used to predict outputs and to capture uncertainty in the predictions. However, the GP regression process becomes computationally intensive when the training spatial dataset has a large number of observations. To address this challenge, we introduce a scalable GP algorithm, termed MuyGPs, which incorporates nearest-neighbor and leave-one-out cross-validation during training. This approach enables the evaluation of large spatial datasets with state-of-the-art accuracy and speed in certain spatial problems. Despite these advantages, conventional quadratic loss functions used in the MuyGPs optimization, such as root mean squared error (RMSE), are highly influenced by outliers. We explore the behavior of MuyGPs in cases involving outlying observations and, subsequently, develop a robust approach to handle and mitigate their impact. Specifically, we introduce a novel leave-one-out loss function based on the pseudo-Huber function (LOOPH) that effectively accounts for outliers in large spatial datasets within the MuyGPs framework. Our simulation study shows that the LOOPH loss method maintains accuracy despite outlying observations, establishing MuyGPs as a powerful tool for mitigating unusual observation impacts in the large data regime. In the analysis of US ozone data, MuyGPs provides accurate predictions and uncertainty quantification, demonstrating its utility in managing data anomalies. Through these efforts, we advance the understanding of GP regression in spatial contexts.

Mukangango, Juliette↗
DOE OSTI2024

Nowcasting Earthquakes With Stochastic Simulations: Information Entropy of Earthquake Catalogs

Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity (“ERAS”) stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori-Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the “non-Poisson” power-law aftershock clustering, implying that the practice of de-clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity.

58 GEOSCIENCES↗
DOE OSTI2024

Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model

Abstract. Emissions of anthropogenic aerosol and their precursors are often prescribed in global aerosol models. Most of these emissions are spatially heterogeneous at model grid scales. When remapped from low-resolution data, the spatial heterogeneity in emissions can be lost, leading to large errors in the simulation. It can also cause the conservation problem if non-conservative remapping is used. The default anthropogenic emission treatment in the Energy Exascale Earth System Model (E3SM) is subject to both problems. In this study, we introduce a revised emission treatment for the E3SM Atmosphere Model (EAM) that ensures conservation of mass fluxes and preserves the original emission heterogeneity at the model-resolved grid scale. We assess the error estimates associated with the default emission treatment and the impact of improved heterogeneity and mass conservation in both globally uniform standard-resolution (∼ 165 km) and regionally refined high-resolution (∼ 42 km) simulations. The default treatment incurs significant errors near the surface, particularly over sharp emission gradient zones. Much larger errors are observed in high-resolution simulations. It substantially underestimates the aerosol burden, surface concentration, and aerosol sources over highly polluted regions, while it overestimates these quantities over less-polluted adjacent areas. Large errors can persist at higher elevation for daily mean estimates, which can affect aerosol extinction profiles and aerosol optical depth (AOD). We find that the revised treatment significantly improves the accuracy of the aerosol emissions from surface and elevated sources near sharp spatial gradient regions, with significant improvement in the spatial heterogeneity and variability of simulated surface concentration in high-resolution simulations. In the next-generation E3SM running at convection-permitting scales where the resolved spatial heterogeneity is significantly increased, the revised emission treatment is expected to better represent the aerosol emissions as well as their lifecycle and impacts on climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ITreeForeCast: An integrated modeling software to simulate tree level growth and forest carbon storage

Healthy trees in forest act as a natural carbon sink, capturing carbon. As they grow, they store carbon in their trunks, leaves and roots. Not all trees store carbon at the same rate, or in the same quantities, as it depends on a variety of biophysical and climatic factors. Furthermore, although carbon estimation in trees can be complex, the precision of estimates is tightly linked to trees growth, both in diameter and height. However, the simulation of carbon uptake by forest and forest growth has each been modeled separately, and independently at differing levels of detail and spatial resolution. In this paper, we introduce ITreeForeCast, a simulation model combining the two types of modeling on a unified platform, enabling the investigation of impacts of management strategies on carbon sequestration and wood products. ITreeForeCast is a user-extendable framework that offers new opportunities to model, simulate, and visualize the dynamics of individual trees in a forest, simulate management strategies over time, and carbon uptake.

09 - BIOMASS FUELS↗
DOE OSTI2024

Fabrication of a Point-Like Transmission Target for Reducing Computed Tomography Imaging Artifacts

In this study, we address the challenge of enhancing image quality and spatial resolution in computed tomography (CT) imaging by introducing simulation and fabrication of high aspect ratio, point-like transmission targets. Utilizing advanced electroplating techniques, traditionally employed in the fabrication of Through Substrate Via (TSV) interconnects for CMOS circuitry, we successfully embed copper targets within silicon substrates. This method allows us to create high-aspect-ratio features specifically designed for X-ray transmission targets, resulting in micro targets that exhibit a volume increase compared to conventional evaporated surface targets. Furthermore, we present simulation results of the X-ray spectrum generated by these targets, demonstrating their potential to significantly improve both image quality and spatial resolution in CT applications. Our findings suggest that leveraging advanced fabrication techniques can open new avenues for the development of enhanced imaging technologies in medical diagnostics and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

High fidelity simulations of contaminant dispersion in an urban environment with comparison to magnetic resonance imaging measurements

The dispersion of a contaminant in an urban environment has the potential to impact a large population of people. In this work, a complex urban canopy flow based on the Oklahoma City downtown business district circa 2003 is studied using Magnetic Resonance Imaging (MRI) and high-fidelity Large Eddy Simulations (LES). MRI is a novel experimental technique that can provide high-resolution measurements in four dimensions (three spatial and temporal) for lab scale models. The experiments and simulations use the same geometry and boundary conditions providing a one-to-one comparison of the two methods. Results are presented on the time-averaged velocity and concentration fields, the temporal dynamics of the concentration plumes for a transient release, and a novel Cloud Identification Algorithm that can separate plumes produced by periodic contaminant releases used for ensemble averaging over many releases. The MRI and LES datasets both include millions of measurement voxels and the comparisons highlight the complex 3D nature of the flow including strong vertical velocities in spanwise street canyons and flow acceleration in streamwise street canyons. The concentration fields are qualitatively similar albeit the LES shows larger dispersion. A quantitative analysis with performance measures compares the datasets pointwise and demonstrates that the two 3D datasets are similar with respect to many measures including a fractional bias of 0.02 (ideal=0.0), correlation coefficient of 0.87 (ideal = 1.0), and the fraction points within a factor of 2 is 0.98 (ideal = 1.0). Plume analysis compares the arrival and residence time of contaminant and is found to vary significantly with location within the urban environment with arrival times between 0 and 1.25 and differences within the contaminant cloud less than 10% at most locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Representing Fine‐Scale Topographic Effects on Surface Radiation Balance in Hyper‐Resolution Land Surface Models

Land surface models are increasingly used to simulate land surface processes at hyper-spatial resolutions (e.g., ∼1 km). As model resolution increases, grid-scale topographic effects on surface radiation fluxes and their interactions between adjacent grids become more pronounced. However, current land surface models routinely neglect the fine-scale topographic effects on surface radiation balance. This study developed physically-based and computationally-efficient parameterizations (fineTOP) that explicitly resolve fine-scale topographic effects on downward shortwave and longwave radiation as well as land surface radiative properties. The newly developed parameterizations were implemented and tested in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Multi-decadal km-resolution ELM simulations over the California Sierra Nevada show that fine-scale topography significantly impacts the surface energy balance and snow processes across seasons. Slope determines the magnitude of topographic effects, while aspect controls their sign. For slopes larger than 30°, topography-induced change in annual surface temperature can be as large as 3.3 K. Regionally, the mean value and standard deviation of topography-induced changes in annual surface temperature are −0.22 ± 0.38 K and +0.25 ± 0.37 K over north-facing and south-facing slopes, respectively. Topography-induced changes in surface radiative properties account for 3.5% ± 13.8% of total topographic effects on annual net radiation. With fineTOP, ELM captures the aspect-dependence of snow cover fraction, snow water equivalent, and land surface temperature found in MODIS satellite observations and a snow reanalysis data set, while the default ELM fails to capture this phenomenon. The enhanced capability to represent fine-scale topographic effects on surface radiation balance can be used to advance understanding of the role of fine-scale topography in land surface processes and land-atmosphere interactions over mountainous regions.

Hao, Dalei [Pacific Northwest National Laboratory ↗
DOE OSTI2025

A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport

High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for applications requiring rapid responses or iterative processes, such as optimization, uncertainty quantification, or inverse modeling. To address this challenge, this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution (DST3D-UNet-SR) model, a highly efficient deep learning model for plume dispersion predictions. DST3D-UNet-SR is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the TM predictions. We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations (LES) of plume transport. We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional (3D) plume dispersion by three orders of magnitude. Additionally, the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data, substantially improving prediction accuracy in high-concentration regions near the source.

3D temporal sequences↗
DOE OSTI2026

Machine learning-based bias-corrected future projections of ozone concentrations from a chemistry-climate model

Reliable projection of future near-surface ozone is crucial for air quality management and health risk assessment. However, potential biases in spatial distribution, magnitude and trends in ozone concentrations simulated by global chemistry-climate models limit their applicability in regional-scale evaluations. In this study, LightGBM, a machine learning (ML) algorithm is applied to correct biases in CESM2-simulated ozone concentrations over China, the United States and Europe and calibrate future ozone projections under two diverse Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) scenarios from 2020 to 2060. The ML-based correction significantly improves the spatial distribution and reduces the model bias by 40%–60%. It also reverses the potentially incorrect trend of ozone change under SSP1-2.6 in eastern China. When applying ML-based bias correction to CESM2 future projections, warm season mean ozone concentrations decrease across China, the United States, and Europe by –13.5, –17.9, and –13.7 µg/m³, respectively, between 2020 and 2060 in SSP1-2.6, while they increase by 9.4, 2.0, and 5.2 µg/m³ in SSP5-8.5. Decomposition analysis show that changes in anthropogenic emissions dominate future ozone changes in both scenarios, while strong climate penalty from ozone changes occurs in polluted eastern China and climate benefit is found in western China, the United States and Europe under SSP5-8.5. These findings demonstrate the value of combining ML with chemistry-climate models to produce more accurate air quality projections, thereby informing more effective and region-specific environmental protection strategies.

Chemistry Model↗