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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
NASA NTRS1990

Spin bipolaron in the framework of emery model for high-T(sub c) copper oxide superconductors

The high-T(sub c) oxide compounds discovered recently exhibit a number of interesting physical properties. Two-dimensional antiferromagnetic spin order has been observed in these materials at the oxygen deficiency. This fact can be explained by strong correlation of the spins, situated on Cu sites in the conducting planes of the oxide superconductors. The doping or the oxygen deficiency lead to the occurrence of holes, occupying the oxygen p-orbitals according to the Emery model. At the small hole concentration they can move along the antiferromagnetic lattice of spins, localized on Cu sites. Researchers consider the two holes situation and describe in what way their behavior depends on the antiferromagnetic exchange interation J. It is known that in the framework of Hubbard model with strong on-site Coulomb repulsion, a single hole can form a spin polaron of the large radius. It is reasonable to admit that two holes with parallel spins (triplet) form the spin bipolaron complex owing to the hole excitations' capability to polarize Cu spin surroundings. Such an excitation was considered in the phenomenological way. Here the problem is discussed on the basis of the microscopic approach in the framework of the variational principle. A special kind of wave function is used for such a purpose. The wave function is constructed by generalizing the trial functions proposed in over two holes excitation situation (triplet) and then the region of spin bipolaron existance in the framework of Emery model is studied. In this model the Hamiltonian can be easily rewritten by forming the oxygen states transforming as the irreducible representations of the group D(sub 4).

Golub, A. A.↗
DOE OSTI2023

Refined spin-wave model and multimagnon bound states in Li 2 CuO 2

Here we report a study of the spin dynamics in the ferromagnetic chain compound Li 2 CuO 2 . Inelastic neutron scattering measurements allow for the spin Hamiltonian to be determined using a J 1 –J 2 XXZ-Heisenberg spin chain model with weak interchain interactions. The primary exchange parameters determined from our data are qualitatively consistent with those of Lorenz et al. [Europhys. Lett. 88, 37002 (2009)], and our data allow for the resolution of additional interchain exchange interactions. We also observe the formation of two- and, potentially, three-magnon bound states. The two-magnon bound state exists only in the magnetically ordered phase of this material, consistent with stabilization by the weak, Ising-like exchange anisotropy of the nearest-neighbor intrachain interaction. In contrast, the potential three-magnon state persists in a finite temperature regime above T N , indicating an unconventional character. Finally, our results establish Li 2 CuO 2 as an experimental platform for the study of exchange anisotropy-stabilized bound states in a ferromagnetic chain.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1978

High pressure phase transitions in tetrahedrally coordinated semiconducting compounds

New experimental results are reported for structural transitions at high pressure in several III-V compounds and two II-VI compounds. These data, together with earlier results, are then compared with the predictions of model calculations of Van Vechten. Experimental transition pressures are often at variance with calculated values. However, his calculation assumes that the high pressure phase is metallic, with the beta-Sn structure. The present results show that several compounds assume an ionic NaCl structure at high pressure, while others have neither the beta-Sn nor NaCl structure.

Yu, S. C.↗
DOE OSTI2024

A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities]

Many scientific applications that support the mission of the US Department of Energy (US-DoE) require modeling complex engineering and/or physical systems. Here, examples of such complex systems arise from: (a) materials science to develop new compounds with exceptional mechanical and thermodynamical properties (e.g., resistance to mechanical stresses and high temperatures), (b) structural and nuclear engineering to model the temporal evolution of the structural damage of concrete shields exposed to continuous neutron and gamma radiations emitted by the nuclear reactor core, (c) urban sciences (e.g., transportation and smart buildings), and (d) power grid systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Covalent Drug Binding in Live Cells Monitored by Mid-Infrared Quantum Cascade Laser Spectroscopy: Photoactive Yellow Protein as a Model System

The detection of drug-target interactions in live cells enables analysis of therapeutic compounds in a native cellular environment. Recent advances in spectroscopy and molecular biology have facilitated the development of genetically encoded vibrational probes like nitriles that can sensitively report on molecular interactions. Nitriles are powerful tools for measuring electrostatic environments within condensed media like proteins, but such measurements in live cells have been hindered by low signal-to-noise ratios. In this study, we design a spectrometer based on a double-beam quantum cascade laser (QCL)-based transmission infrared (IR) source with balanced detection that can significantly enhance sensitivity to nitrile vibrational probes embedded in proteins within cells compared to a conventional FTIR spectrometer. Here, using this approach, we detect small-molecule binding in Escherichia coli, with particular focus on the interaction between para-Coumaric acid (pCA) and nitrile-incorporated photoactive yellow protein (PYP). This system effectively serves as a model for investigating covalent drug binding in a cellular environment. Notably, we observe large spectral shifts of up to 15 cm –1 for nitriles embedded in PYP between the unbound and drug-bound states directly within bacteria, in agreement with observations for purified proteins. Such large spectral shifts are ascribed to the changes in the hydrogen-bonding environment around the local environment of nitriles, accurately modeled through high-level molecular dynamics simulations using the AMOEBA force field. Our findings underscore the QCL spectrometer’s ability to enhance sensitivity for monitoring drug–protein interactions, offering new opportunities for advanced methodologies in drug development and biochemical research.

chromophores↗
DOE OSTI2023

Elucidating the photodissociation fingerprint and quantifying the determination of organic hydroperoxides in gas-phase autoxidation

Hydroperoxides are formed in the atmospheric oxidation of volatile organic compounds, in the combustion autoxidation of fuel, in the cold environment of the interstellar medium, and also in some catalytic reactions. They play crucial roles in the formation and aging of secondary organic aerosols and in fuel autoignition. However, the concentration of organic hydroperoxides is seldom measured, and typical estimates have large uncertainties. In this work, we developed a mild and environmental-friendly method for the synthesis of alkyl hydroperoxides (ROOH) with various structures, and we systematically measured the absolute photoionization cross-sections (PICSs) of the ROOHs using synchrotron vacuum ultraviolet-photoionization mass spectrometry (SVUV-PIMS). A chemical titration method was combined with an SVUV-PIMS measurement to obtain the PICS of 4-hydroperoxy-2-pentanone, a typical molecule for combustion and atmospheric autoxidation ketohydroperoxides (KHPs). We found that organic hydroperoxide cations are largely dissociated by loss of OOH. This fingerprint was used for the identification and accurate quantification of the organic peroxides, and it can therefore be used to improve models for autoxidation chemistry. The synthesis method and photoionization dataset for organic hydroperoxides are useful for studying the chemistry of hydroperoxides and the reaction kinetics of the hydroperoxy radicals and for developing and evaluating kinetic models for the atmospheric autoxidation and combustion autoxidation of the organic compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R . palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R . palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R . palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R . palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R . palustris Bis A53 ( i DT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. i DT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Compositional Tuning of Magnetic Properties in a Series of Transition Metal Site-Deficient UCo x Bi 2 and UNi x Bi 2 Phases

Varying the electronic structure of topological materials through aliovalent substitution is a primary approach to tuning their physical properties. Unlike substitution, metal site deficiency intrinsic to some structure types, including HfCuSi 2 -type, has rarely been employed for controlling the properties of topological phases. In this report, we describe the synthesis and characterization of two new series of compounds, UCoxBi 2 and UNixBi 2 , which demonstrate the variation of transition metal content through synthetic conditions. Magnetic measurements reveal the dependence between the extent of transition metal incorporation and the magnetism of the resulting phase. DFT calculations demonstrated the ability to model their formation and predict the stability ranges of transition metal-site deficient compounds.

Crystal structure↗
DOE OSTI2022

Investigating coastal backwater effects and flooding in the coastal zone using a global river transport model on an unstructured mesh

Abstract. Coastal backwater effects are caused by the downstream water level increase as a result of elevated sea level, high river discharge and their compounding influence. Such effects have crucial impacts on floods in densely populated regions but have not been well represented in large-scale river models used in Earth system models (ESMs), partly due to model mesh deficiency and oversimplifications of river hydrodynamics. Using two mid-Atlantic river basins as a testbed, we perform the first attempt to simulate the backwater effects comprehensively over a coastal region using the MOSART river transport model under an ESM framework, i.e., Energy Exascale Earth System Model (E3SM) configured on a regionally refined unstructured mesh, with a focus on understanding the backwater drivers and their long-term variations. By including sea level variations at the river downstream boundary, the model performance in capturing backwaters is greatly improved. We also propose a new flood event selection scheme to facilitate the decomposition of backwater drivers into different components. Our results show that while storm surge is a key driver, the influence of extreme discharge cannot be neglected, particularly when the river drains to a narrow river-like estuary. Compound flooding, while not necessarily increasing the flood peaks, exacerbates the flood risk by extending the duration of multiple coastal and fluvial processes. Furthermore, our simulations and analysis highlight the increasing strength of backwater effects due to sea level rise and more frequent storm surge during 1990–2019. Thus, backwaters need to be properly represented in ESMs to improve the predictive understanding of coastal flooding.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Development and Application of Computational/In Vitro Toxicological Methods for Chemical Hazard Risk Reduction of New Materials for Advanced Weapon Systems

The development of quantitative structure-activity relationship (QSAR) is essential for reducing the chemical hazards of new weapon systems. The current collaboration between HEST (toxicology research and testing), MLPJ (computational chemistry) and PRS (computational chemistry, new propellant synthesis) is focusing R&D efforts on basic research goals that will rapidly transition to useful products for propellant development. Computational methods are being investigated that will assist in forecasting cellular toxicological end-points. Models developed from these chemical structure-toxicity relationships are useful for the prediction of the toxicological endpoints of new related compounds. Research is focusing on the evaluation tools to be used for the discovery of such relationships and the development of models of the mechanisms of action. Combinations of computational chemistry techniques, in vitro toxicity methods, and statistical correlations, will be employed to develop and explore potential predictive relationships; results for series of molecular systems that demonstrate the viability of this approach are reported. A number of hydrazine salts have been synthesized for evaluation. Computational chemistry methods are being used to elucidate the mechanism of action of these salts. Toxicity endpoints such as viability (LDH) and changes in enzyme activity (glutahoione peroxidase and catalase) are being experimentally measured as indicators of cellular damage. Extrapolation from computational/in vitro studies to human toxicity, is the ultimate goal. The product of this program will be a predictive tool to assist in the development of new, less toxic propellants.

Frazier, John M.↗
NASA NTRS2003

Seasonal Variation and Ecosystem Dependence of Emission Factors for Selected Trace Gases and PM2.5 for Southern African Savanna Fires

In this paper we present the first early dry season (early June-early August) emission factor measurements for carbon dioxide (CO2), carbon monoxide (CO), methane (Ca), nonmethane hydrocarbons (NMHC), and particulates with a diameter less than 2.5 microns (pM2.5) for southern African grassland and woodland fires. Seasonal emission factors for grassland fires correlate linearly with the proportion of green grass, used as a surrogate for the fuel moisture content, and are higher for products of incomplete combustion in the early part of the dry season compared with later in the dry season. Models of emission factors for NMHC and PM(sub 2.5) versus modified combustion efficiency (MCE) are statistically different in grassland compared with woodland ecosystems. We compare predictions based on the integration of emissions factors from this study, from the southern African Fire-Atmosphere Research Initiative 1992 (SAFARI-92), and from SAFARI-2000 with those based on the smaller set of ecosystem-specific emission factors to estimate the effects of using regional-average rather than ecosystem-specific emission factors. We also test the validity of using the SAFARI-92 models for emission factors versus MCE to predict the early dry season emission factors measured in this study. The comparison indicates that the largest discrepancies occur at the low end (0.907) and high end (0.972) of MCE values measured in this study. Finally, we combine our models of MCE versus proportion of green grass for grassland fires with emission factors versus MCE for selected oxygenated volatile organic compounds measured in the SAFARI-2000 campaign to derive the first seasonal emission factors for these compounds. The results of this study demonstrate that seasonal variations in savanna fire emissions are important and should be considered in modeling emissions at regional to continental scales.

Korontzi, S.↗
NASA NTRS2021

Climate-driven Chemistry and Aerosol Feedbacks in CMIP6 Earth System Models

Feedbacks play a fundamental role in determining the magnitude of the response of the climate system to external forcing, such as from anthropogenic emissions. The latest generation of Earth system models includes aerosol and chemistry components that interact with each other and with the biosphere. These interactions introduce a complex web of feedbacks that is important to understand and quantify. This paper addresses multiple pathways for aerosol and chemical feedbacks in Earth system models. These focus on changes in natural emissions (dust, sea salt, dimethyl sulfide, biogenic volatile organic compounds (BVOCs) and lightning) and changes in reaction rates for methane and ozone chemistry. The feedback terms are then given by the sensitivity of a pathway to climate change multiplied by the radiative effect of the change. We find that the overall climate feedback through chemistry and aerosols is negative in the sixth Coupled Model Intercomparison Project (CMIP6) Earth system models due to increased negative forcing from aerosols in a climate with warmer surface temperatures following a quadrupling of CO2 concentrations. This is principally due to increased emissions of sea salt and BVOCs which are sensitive to climate change and cause strong negative radiative forcings. Increased chemical loss of ozone and methane also contributes to a negative feedback. However, overall methane lifetime is expected to increase in a warmer climate due to increased BVOCs. Increased emissions of methane from wetlands would also offset some of the negative feedbacks. The CMIP6 experimental design did not allow the methane lifetime or methane emission changes to affect climate, so we found a robust negative contribution from interactive aerosols and chemistry to climate sensitivity in CMIP6 Earth system models.

Aerosol feedbacks↗
DOE OSTI2025

A novel methodology for assessing the hygroscopicity of aerosol filter samples

Abstract. Due to US regulations, concentrations of hygroscopic inorganic sulfate and nitrate have declined in recent years, leading to an increased importance of the hygroscopic nature of organic matter (OM). The hygroscopicity of OM is poorly characterized because only a fraction of the multitude of organic compounds in the atmosphere is readily measured, and there is limited information on their hygroscopic behaviors. Hygroscopicity of aerosol is traditionally measured using a humidified tandem differential mobility analyzer (HTDMA) or electrodynamic balance (EDB). EDB measures water uptake by a single particle. For ambient and chamber studies, HTDMA measurements provide water uptake and particle size information but not chemical composition. To fill this information gap, we developed a novel methodology to assess the water uptake by particles collected on Teflon filters. This method uses the same filter sample for both hygroscopicity measurements and chemical characterization, thereby providing an opportunity to link the measured hygroscopicity with ambient particle composition. To test the method, hygroscopic measurements were conducted in the laboratory for ammonium sulfate, sodium chloride, glucose, and malonic acid, which were collected on 25 mm Teflon filters using an aerosol generator and sampler. Constant-humidity solutions (CHSs), including potassium chloride, barium chloride dihydrate, and potassium sulfate, were employed in a saturated form to maintain the relative humidity (RH) at approximately 84 %, 90 %, and 97 % in small chambers. Our preliminary experiments revealed that, without the pouch, water uptake measurements were not feasible due to rapid water loss during weighing. Additionally, we observed some absorption by the aluminum pouch itself. To account for this, concurrent measurements were conducted for both the loaded and the blank filters at each RH level. Thus, the dry loaded and blank Teflon filters were placed in aluminum pouches with one side open and in RH-controlled chambers for more than 24 h. The wet loaded samples and wet blanks were then weighed using an ultramicrobalance to determine the water uptake by the respective compound and the blank Teflon filter. The net amount of water absorbed by each compound was calculated by subtracting the water uptake of the blank filter from that of the wet loaded filter. Hygroscopic parameters, including the water-to-solute (W / S) ratio, molality, mass fraction solute (mfs), and growth factors (GFs), were calculated from the measurements. The results obtained are consistent with those reported by the Extended Aerosol Inorganics Model (E-AIM) and previous studies utilizing HTDMA and EDB for these compounds, highlighting the accuracy of this new methodology. This new approach enables the hygroscopicity and chemical composition of individual filter samples to be assessed so that in complex mixtures, such as chamber and ambient samples, the total water uptake can be parsed between the inorganic and organic components of the aerosol.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Model-based compositional predictions for a differential scanning calorimetry/thermogravimetric analysis-mass spectrometry system used for heat of vaporization measurements

A Differential Scanning Calorimetry/Thermogravimetric Analysis (DSC/TGA) procedure for determination of Heat of Vaporization (HOV) of fuel and surrogate fuel mixtures has been previously utilized for evaluation of oxygenate impacts on spark ignition fuels. This analysis has been further leveraged with mass spectrometry (MS) to elucidate vapor phase compositional changes during evaporation. In this study we focus on a simple system of three compounds (ethanol, n-hexane, and n-octane) to provide a model-based understanding of the evaporation process under these experimental conditions. An Aspen Plus model was set up to provide liquid and vapor phase concentration predictions during mixture evaporation using the initial mixture composition, and measured temperature and mass loss profiles from DSC/TGA experiments. Agreements between the trends of the MS ion count profiles and model composition predictions provide a degree of validation of this modeling approach that can allow the interpretation and extrapolation of DSC/TGA measurements for more complex fuel mixtures.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Amplified risk of spatially compounding droughts during co-occurrences of modes of natural ocean variability

Abstract Spatially compounding droughts over multiple regions pose amplifying pressures on the global food system, the reinsurance industry, and the global economy. Using observations and climate model simulations, we analyze the influence of various natural Ocean variability modes on the likelihood, extent, and severity of compound droughts across ten regions that have similar precipitation seasonality and cover important breadbaskets and vulnerable populations. Although a majority of compound droughts are associated with El Niños, a positive Indian Ocean Dipole, and cold phases of the Atlantic Niño and Tropical North Atlantic (TNA) can substantially modulate their characteristics. Cold TNA conditions have the largest amplifying effect on El Niño-related compound droughts. While the probability of compound droughts is ~3 times higher during El Niño conditions relative to neutral conditions, it is ~7 times higher when cold TNA and El Niño conditions co-occur. The probability of widespread and severe compound droughts is also amplified by a factor of ~3 and ~2.5 during these co-occurring modes relative to El Niño conditions alone. Our analysis demonstrates that co-occurrences of these modes result in widespread precipitation deficits across the tropics by inducing anomalous subsidence, and reducing lower-level moisture convergence over the study regions. Our results emphasize the need for considering interactions within the larger climate system in characterizing compound drought risks rather than focusing on teleconnections from individual modes. Understanding the physical drivers and characteristics of compound droughts has important implications for predicting their occurrence and characterizing their impacts on interconnected societal systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Implementing and Improving CBMZ-MAM3 Chemistry and Aerosol Modules in the Regional Climate Model WRF-CAM5: An Evaluation over the Western US and Eastern North Pacific

The representation of aerosols in climate-chemistry models is important for air quality and climate change research, but it can require significant computational resources. The objective of this study was to improve the representation of aerosols in climate–chemistry models, specifically in the carbon bond mechanism, version Z (CBMZ), and modal aerosol modules with three lognormal modes (MAM3) in the WRF-CAM5 model. The study aimed to enhance the model’s chemistry capabilities by incorporating biomass burning emissions, establishing a conversion mechanism between volatile organic compounds (VOCs) and secondary organic carbons (SOCs), and evaluating its performance against observational benchmarks. The results of the study demonstrated the effectiveness of the enhanced chemistry capabilities in the WRF-CAM5 model. Six simulations were conducted over the western U.S. and northeastern Pacific region, comparing the model’s performance with observational benchmarks such as reanalysis, ground-based, and satellite data. The findings revealed a significant reduction in root-mean-square errors (RMSE) for surface concentrations of black carbon (BC) and organic carbon (OC). Specifically, the model exhibited a 31% reduction in RMSE for BC concentrations and a 58% reduction in RMSE for OC concentrations. These outcomes underscored the importance of accurate aerosol representation in climate-chemistry models and emphasized the potential for improving simulation accuracy and reducing errors through the incorporation of enhanced chemistry modules in such models.

54 ENVIRONMENTAL SCIENCES↗