Coupled Flow, Transport, and Biogeochemistry Subsurface Modeling to Inform Arctic Methane Emission Predictions
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Bigler, Lisa Amelia↗
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Historical Emissions - no seasonality
Version of previously released anthropogenic SO2 and BC emissions data re-generated with no seasonality for use in the Emissions-MIP model inter-comparison project.
99 GENERAL AND MISCELLANEOUS↗
Imaging of Fibroblast Activation Protein Alpha Expression in a Preclinical Mouse Model of Glioma Using Positron Emission Tomography
Glioblastoma multiforme (GBM) is the most aggressive glioma of the primary central nervous system. Due to the lack of effective treatment options, the prognosis for patients remains bleak. Fibroblast activation protein alpha (FAP), a 170 kDa type II transmembrane serine protease was observed to be expressed on glioma cells and within the glioma tumor microenvironment. To understand the utility of targeting FAP in this tumor type, the immuno-PET radiopharmaceutical [ 89 Zr]Zr-Df-Bz-F19 mAb was prepared and Lindmo analysis was used for its in vitro evaluation using the U87MG cell line, which expresses FAP endogenously. Lindmo analysis revealed an association constant (K a ) of 10 -8 M -1 and an immunoreactivity of 52%. Biodistribution studies in U87MG tumor-bearing mice revealed increasing radiotracer retention in tumors over time, leading to average tumor-to-muscle ratios of 3.1, 7.3, 7.2, and 8.3 at 2, 24, 48 and 72 h, respectively. Small animal PET corroborated the biodistribution studies; tumor-to-muscle ratios at 2, 24, 48, and 72 h were 2.0, 5.0, 6.1 and 7.8, respectively. Autoradiography demonstrated accumulated activity throughout the interior of FAP + tumors, while sequential tumor sections stained positively for FAP expression. Conversely, FAP - tissues retained minimal radioactivity and were negative for FAP expression by immunohistochemistry. These results demonstrate FAP as a promising biomarker that may be exploited to diagnose and potentially treat GBM and other neuroepithelial cancers.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The Influence of β-decay Rates on r-process Observables
Abstract The rapid neutron capture process ( r -process) is one of the main mechanisms whereby elements heavier than iron are synthesized, and is entirely responsible for the natural production of the actinides. Kilonova emissions are modeled as being largely powered by the radioactive decay of species synthesized via the r -process. Given that the r -process occurs far from nuclear stability, unmeasured beta-decay rates play an essential role in setting the timescale for the r -process. In an effort to better understand the sensitivity of kilonova modeling to different theoretical global beta-decay descriptions, we incorporate these into nucleosynthesis calculations. We compare the results of these calculations and highlight differences in kilonova nuclear energy generation and light-curve predictions, as well as final abundances and their implications for nuclear cosmochronometry. We investigate scenarios where differences in beta-decay rates are responsible for increased nuclear heating on timescales of days that propagates into a significantly increased average bolometric luminosity between 1 and 10 days post-merger. We identify key nuclei, both measured and unmeasured, whose decay rates directly impact nuclear heating generation on timescales responsible for light-curve evolution. We also find that uncertainties in beta-decay rates significantly impact age estimates from cosmochronometry.
79 ASTRONOMY AND ASTROPHYSICS↗
Origin of the ring ellipticity in the black hole images of M87*
We investigate the origin of the elliptical ring structure observed in the images of the supermassive black hole M87*, aiming to disentangle contributions from gravitational, astrophysical, and imaging effects. Leveraging the enhanced capabilities of the Event Horizon Telescope (EHT)'s 2018 array, including improved (u,v)-coverage from the Greenland Telescope, we measured the ring's ellipticity using five independent imaging methods, obtaining a consistent average value of τ = 0.08−0.02+0.03 with a position angle of ξ = 50.1−7.6+6.2 degrees. To interpret this measurement, we compared it to general relativistic magnetohydrodynamic (GRMHD) simulations spanning a wide range of physical parameters including the thermal or nonthermal electron distribution function, spins, and ion-to-electron temperature ratios in both low- and high-density regions. We find no statistically significant correlation between spin and ellipticity in GRMHD images. Instead, we identify a correlation between ellipticity and the fraction of non-ring emission, particularly in nonthermal models and models with higher jet emission. These results indicate that the ellipticity measured from the M87* emission structure is consistent with that expected from simulations of turbulent accretion flows around black holes, where it is dominated by astrophysical effects rather than gravitational ones. Future high-resolution imaging, including space very long baseline interferometry and long-term monitoring, will be essential to isolate gravitational signatures from astrophysical effects.Key words: accretion, accretion disks / black hole physics / gravitation / galaxies: active** These authors have contributed equally to this work.*** NASA Hubble Fellowship Program, Einstein Fellow.† Deceased.
Dahale, Rohan [Granada U., Theor. Phys. Astrophys.↗
Integrated Modeling of U.S. Agricultural Soil Emissions of Reactive Nitrogen and Associated Impacts on Air Pollution, Health, and Climate
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54 ENVIRONMENTAL SCIENCES↗
A Thick-Target Bremsstrahlung Model with Angularly-Dependent Emission in the MCNP6 Code
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97 MATHEMATICS AND COMPUTING↗
Improved Representations of Longwave Surface Emissivity to Reduce Surface and Atmospheric Heating Biases in Earth System Models
Abstract Many Earth system models (ESMs) approximate surface emissivity as a broadband constant. This approximation reduces the computational burden, yet omits the spectral structure of emissivity and atmospheric absorption. Neglecting spectral variation in surface emission introduces biases in longwave (LW) atmospheric fluxes and heating. Biases are strongest over surfaces with strongly varying emissivity and minimal atmospheric opacity. We examine these biases over water, ice, and snow surfaces. We partition spectral emissivity into the 16 spectral bands utilized by a single‐column atmospheric radiative transfer model (RRTMG_LW) commonly used in ESMs. We quantify flux and heating biases introduced by broadband assumptions relative to the spectrally resolved case for standard atmospheric profiles over each surface type. Current assumptions tend to overestimate upwelling surface fluxes; for example, the greybody assumption overestimates flux by 1.6 W/m 2 (0.52%) at the bottom of a mid‐latitude winter atmosphere over ice, and by 2.33 (1.0%) at the top of atmosphere. The blackbody assumption tends to artificially cool Earth's surface, stabilizing the lower troposphere. Interestingly, the optimal broadband emissivity can deviate from the Planck‐weighted mean by up to 3% depending on surface type and atmospheric profile. We investigate bias sensitivity to surface temperature, cloud water path, and atmospheric water vapor. Bias is most sensitive to water vapor content, and least sensitive to cloud water path. Lastly, we show that a modified greybody method with updated broadband values can reduce total surface flux bias up to 1.69 , comparable to a five‐band approach and at a fraction of the computational cost.
Manzo, L. [Department of Earth System Science Univ↗
Turbulent Model of Crab Nebula Radiation
We construct a turbulent model of the Crab Nebula's nonthermal emission. The present model resolves a number of long-standing problems of the Kennel–Coroniti model: (i) the sigma problem, (ii) the hard spectrum of radio electrons, (iii) the high peak energy of gamma-ray flares, (iv) and the spatial evolution of the infrared (IR) emission. The Nebula contains two populations of injected particles: Component-I, accelerated at the wind termination shock via the Fermi-I mechanism; and Component-II, accelerated in reconnecting turbulence in highly magnetized (σ >> 1) plasma in the central part of the Crab Nebula. The reconnecting turbulence in Component-II extends from radio to gamma-rays: it accelerates radio electrons with a hard spectrum, destroys the large-scale magnetic flux (and thus resolves the sigma problem), and occasionally produces gamma-ray flares (from the largest-scale reconnection events). The model reproduces the broadband spectrum of the Crab Nebula, from low-frequency synchrotron emission in radio to inverse-Compton emission at TeV energies, as well as the spatially resolved evolution of the spectral indices in the IR and optical bands.
79 ASTRONOMY AND ASTROPHYSICS↗
Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models
Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r >0.95 for ML and r =0.14~0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r =0.74 for ML and r <0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.
54 ENVIRONMENTAL SCIENCES↗
Harmonizing tau positron emission tomography in Alzheimer's disease: The CenTauR scale and the joint propagation model
Abstract INTRODUCTION Tau‐positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis. METHODS Using head‐to‐head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed “CenTauR” [CTR]). JPM is a statistical model that simultaneously models the relationships between head‐to‐head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale. RESULTS A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach. DISCUSSION Preliminary findings using the JPM support the development and adoption of a universal scale for tau‐PET quantification. Highlights Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed “CenTauRs”. Tested a Centiloid‐like linear regression approach. Using five cohorts with head‐to‐head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.
Neurosciences & Neurology↗
OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions – implementation in a global climate model and impacts on clouds
Sea spray aerosol is one of the major sources of atmospheric particulate matter globally. It has increasingly been recognized that organic matter derived from ocean biological precursors contributes significantly to the composition of submicron sea spray and may modify sea spray aerosol impacts on clouds and climate. This paper describes the implementation of the OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) parameterization for sea spray organic aerosol emissions in a global Earth system model, the Energy Exascale Earth System Model (E3SM). OCEANFILMS is a physically based model that links sea spray chemistry with ocean biogeochemistry using a Langmuir partitioning approach. We describe the implementation details of OCEANFILMS within E3SM, compare simulated aerosol fields with observations, and investigate impacts on simulated clouds and climate. Four sensitivity cases are tested, in which organic emissions either strictly add to or strictly replace sea salt emissions (in mass and number) and are either fully internally or fully externally mixed with sea salt. The simulation with internally mixed, added organics agrees reasonably well with observed seasonal cycles of organic matter in marine aerosol and has been selected as the default configuration of the E3SM. In this configuration, sea spray organic aerosol contributes an additional source of cloud condensation nuclei, adding up to 30 cm -3 to Southern Ocean boundary-layer cloud condensation nuclei concentrations (supersaturation = 0.1 %). The addition of this new aerosol source strengthens shortwave radiative cooling by clouds by -0.36 W m -2 in the global annual mean and contributes more than -3.5 W m -2 to summertime zonal mean cloud forcing in the Southern Ocean, with maximum zonal mean impacts of about -4 W m -2 around 50–60°S. This is consistent with a previous top-down, satellite-based empirical estimate of the radiative forcing by sea spray organic aerosol over the Southern Ocean. Through its mechanistic approach, OCEANFILMS offers a path towards improved understanding of the feedbacks between ocean biology, sea spray organic matter, and climate.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Utilizing data-based modeling with low life cycle GHG emissions algae biofuels for engine optimization
Aquatic microalgae are a highly promising feedstock for the production of biocrude and tailored biofuels, with distinct advantages over traditional terrestrial crops, such as reduced land use and avoidance of food production competition. However, unlocking their full potential requires the development of biofuels with low life cycle greenhouse emissions biofuels, such as algae biofuels, which can significantly reduce the environmental impact of the transportation systems without requiring a complete overhaul of existing engine technology. In this study, we employ cutting-edge data-based AI modeling techniques to optimize the performance of heavy-duty engines, with a focus on transitioning towards biofuels with low life cycle greenhouse emissions biofuels. Our methodology offers significant advantages over traditional sweep testing, enabling efficient and accurate optimization of engine performance with minimal time and resources consumption. Our findings demonstrate the potential of utilizing this approach, with up to 55% NOx emissions reductions and up to 2% reduction in fuel consumption compared to the baseline optimized point. Moving forward, we plan to utilize a 30% blend of algae biofuels with diesel fuel, with the ultimate goal of achieving up to 60% lifecycle GHG emissions. Lastly, we plan to compare the results with 100% renewable biodiesel to add an additional dimension of investigating the impact of fuel chemistry on engine optimization. Overall, this study underscores the vital importance of biofuels for reducing the carbon footprint of the transportation sector and supporting a sustainable future. By harnessing the power of data-based AI modeling with low life cycle greenhouse emissions biofuels, we can accelerate the adoption of more environmentally friendly transportation systems and reduce their impact on the planet. Our findings contribute to this transition and offer insights for developing efficient and effective strategies for addressing global climate change.
09 BIOMASS FUELS↗
Exploiting SMILEs and the CMIP5 Archive to Understand Arctic Climate Change Seasonality and Uncertainty
Arctic Amplification (AA) exhibits a distinct seasonal dependence; it is weakest in boreal summer and strongest in winter. Here, we analyze simulations from single-model initial-condition large ensembles and Coupled Model Intercomparison Project Phase 5 to decipher the seasonal evolution of Arctic climate change. Models agree that the annual maximum AA shifts from autumn into winter over the 21st century, accompanied by similar shifts in sea-ice loss and surface turbulent heat fluxes, whereas the maximum precipitation shifts only into late autumn. However, the exact seasonal timing and magnitude of these shifts are highly uncertain. Decomposing the uncertainty into model structural differences, emission scenarios, and internal variability reveals that model differences dominate the total uncertainty, which also undergo autumn-to-winter shifts. We also find that the scenario uncertainty is unimportant for projections of AA. These results highlight that understanding model differences is critical to reducing uncertainty in projected Arctic climate change.
54 ENVIRONMENTAL SCIENCES↗
Seamlessly Fuel Flexible Heat Pump with Optimal Model-based Control Strategies to Reduce Peak Demand, Utility Cost and CO2 Emission
This research develops a novel hybrid fuel heat pump system for space heating of residential and small commercial buildings with built-in optimization and control. Whereas conventional dual fuel systems either run on gas or electricity at any given moment, the proposed seamlessly fuel flexible heat pump (SFFHP) simultaneously consumes gas and electricity and continuously optimizes the proportion of each. The building air flows across the heat pump condenser first and then flows across the furnace coil, and this reduces the heat pump temperature lift. The SFFHP delivers energy savings by allowing each subsystem (gas furnace and electric heat pump) to operate where it performs best to improve energy efficiency, minimize energy cost, and minimize carbon footprint. The capacities of the electric heat pump and gas furnace are continuously adjusted based on ambient conditions, utility price signals, and marginal grid emission signals. An optimal model predictive control strategy was developed with the goal of minimizing utility cost and minimizing CO2 emission. Two case studies were conducted to simulate the performance of SFFHP during the heating season in Chicago and Los Angeles, respectively. Compared with a conventional electric heat pump, SFFHP yields 33% utility cost reduction and 49% CO2 emission reduction in Chicago. Similarly, it achieves 23% utility cost reduction and 17% CO2 emission reduction in Los Angeles. Case studies demonstrate that SFFHP can deliver significant reductions in peak demand, utility cost, and CO2 emission. Due to the hybrid fuel nature of this novel equipment, user comfort will always be maintained. The fuel flexibility makes it an attractive option for demand response programs.
Li, Zhenning↗
Air Dispersion Modeling for the Idaho National Laboratory Permit to Construct P-2020.0045 Facility Emission Cap Revision Request
The U.S. Department of Energy Idaho Operations Office (DOE-ID) is seeking to modify permit to construct (PTC) P-2020.0045 issued to U S Dept. of Energy – Idaho National Laboratory (INL) on January 29, 2021 by the Idaho Department of Environmental Quality (DEQ). This PTC allowed the INL to become a synthetic minor source of air pollutants using a facility emission cap (FEC). Air dispersion modeling was performed as part of the permit revision application process to demonstrate that the modifications will comply with the National Ambient Air Quality Standards (NAAQS). This report documents the modeling methodology and complete results for the air dispersion impact analysis and is supplied as an appendix to the permit revision request, Idaho National Laboratory Permit to Construct P-2020.0045 (INL 2025). All criteria air pollutants (CAPs) regulated under Section 109 of the Clean Air Act were modeled with the exception of lead (Pb) and ozone which are not required to be modeled by DEQ. Consistent with the original permit application, modeling was not performed for toxic air pollutants (TAPs) as uncontrolled emissions did not exceed screening emission levels for carcinogenic and non-carcinogenic TAPs. Acceptable model design values (concentrations) for each CAP and averaging period were summed with average background concentrations and compared to NAAQS. The results demonstrate the impacts of requested FEC limits for CAPs are less than applicable standards and demonstrate emissions up to FEC limits will not cause a violation of ambient air quality standards.
54 ENVIRONMENTAL SCIENCES↗
Air Dispersion Modeling for the Idaho National Laboratory Permit to Construct P-2015.0023 Facility Emission Cap Revision Request
The U.S. Department of Energy Idaho Operations Office (DOE-ID) is seeking to modify permit to construct (PTC) P-2015.0023 issued to U S Dept. of Energy – Idaho National Laboratory (INL) on January 11, 2018 by the Idaho Department of Environmental Quality (DEQ). This PTC allowed the INL to become a synthetic minor source of air pollutants using a facility emission cap (FEC). Air dispersion modeling was performed as part of the permit revision application process to demonstrate that the modifications will comply with the National Ambient Air Quality Standards (NAAQS). This report documents the modeling methodology and complete results for the air dispersion impact analysis and is supplied as an appendix to the permit revision request, Idaho National Laboratory Permit to Construct P-2015.0023 Facility Emission Cap Revision Request (DOE-ID 2020). All CAPs regulated under Section 109 of the Clean Air Act were modeled with the exception of lead (Pb) and ozone which are not required to be modeled by DEQ. Consistent with the original permit application, modeling was not performed for toxic air pollutants (TAPs) as uncontrolled emissions did not exceed screening emission levels for carcinogenic and non-carcinogenic TAPs. Acceptable model design values (concentrations) for each CAP and averaging period were summed with average background concentrations and compared to NAAQS. The results demonstrate the impacts of requested FEC limits for CAPs are less than applicable standards and demonstrate emissions up to FEC limits will not cause a violation of ambient air quality standards.
99 GENERAL AND MISCELLANEOUS↗
Fossil fuel CO 2 emissions over metropolitan areas from space: A multi-model analysis of OCO-2 data over Lahore, Pakistan
Urban areas, where gathering more than 55% of the global population, alone contributed to more than 70% of anthropogenic fossil fuel carbon dioxide (CO 2ff ) emissions. Accurate quantification of CO 2ff emissions from urban areas is of great importance to the formulation of global warming mitigation policies to achieve carbon neutrality by 2050. Satellite-based inversion techniques are unique among “top-down” approaches, potentially allowing us to track CO 2ff emission changes over cities globally. However, its accuracy is still limited by incomplete background information, cloud blockages, aerosol contaminations, and uncertainties in models and emission inventories used as prior. To evaluate the current potential of space-based quantification techniques, we present the first attempt to monitor long-term changes in CO 2ff emissions based on the OCO-2 satellite measurements of column-averaged dry-air mole fractions of CO 2 (X CO2 ) over a fast-growing Asian metropolitan area: Lahore, Pakistan. We first examined the OCO-2 data availability at global scale. About 17% of OCO-2 soundings are marked as high-quality soundings by quality flags over the global 70 most populated cities over the period 2014-2019. Cloud blockage and aerosol contamination are the two main causes of data loss. As an attempt to recover additional soundings, we evaluated the effectiveness of OCO-2 quality flags at the city level by comparing three flux quantification methods (WRF-Chem, X-STILT, and flux cross-sectional integration method), all based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) product. The satellite/bottom-up emissions (OCO-2/ODIAC) ratios of the high-quality tracks better converged across the three methods compared to the all-data tracks with reduced uncertainties in emissions. Thus, OCO-2 quality flags are useful filters of low-quality OCO-2 retrievals at local scales, although originally designed for global-scale studies. All three methods consistently suggested that the ratio medians are greater than 1, which implies that the ODIAC slightly underestimated the CO2ff emissions over Lahore. Additionally, our estimation of the a posteriori CO2ff emission trend was about 734 kt C/year (i.e., an annual 6.7% increase). 10,000 Monte Carlo simulations of the Mann-Kendall upward trend test showed that less than 10% prior uncertainty for 8 tracks (or less than 20% prior uncertainty for 25 tracks) is required to achieve a greater-than-50% trend significant possibility at a 95% confidence level. It implies that the trend is driven by the prior and not due to the assimilation of OCO-2 retrievals. The key to improving the role of satellite data in CO 2 emission trend detection lies in collecting more frequent high-quality tracks near metropolitan areas to achieve significant constraints from X CO2 retrievals.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗