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NASA NTRSDate not supplied

Sub-City-Scale Air Quality Forecasts Combining Models, Satellites, and Surface Measures

Poor air quality is a global major concern, especially in cities with their higher emissions and large numbers of exposed people. Air quality monitoring has traditionally relied on ground-based measurements from a few accurate but expensive regulatory-grade monitors, leading to limited spatial data coverage. More recently, these have been supplemented with other data sources, including satellite observations of pollutants, atmospheric chemistry model simulations, and low-cost monitors allowing for denser spatial data collection at the expense of lower accuracy compared to regulatory-grade monitors. Each of these air quality data sources have their own benefits and drawbacks, and so there is an opportunity to combine these data together while respecting their relative strengths and weaknesses in order to generate a more comprehensive and detailed picture of local air quality. I will present our current work towards such a combination, with a focus on producing higher spatial resolution estimates and near-term forecasts of Nitrogen Dioxide in urban areas in the United States. Our method combines data from the GEOS Composition Forecasting (GEOS-CF) model system, TROPOMI tropospheric NO2 satellite data products, and ground measurements from the EPA regulatory monitoring network using a combination of simple intuitive relationships and machine learning techniques. I will show the performance of this proposed method in several urban areas in the United States, comparing it with baseline approaches using single data sources separately. Overall, we find that combining these disparate datasets together leads to more accurate air quality forecasts in the target areas than is possible using each data source separately.

Air quality↗

NASA NTRS2021

Transition Analysis for the CRM-NLF Wind Tunnel Configuration using Transport Equation Models and Linear Stability Correlations

Transition models based on auxiliary transport equations augmenting the Reynolds-averaged Navier-Stokes (RANS) framework rely upon transition correlations that were derived from a limited number of low-speed experiments. Furthermore, these models often account for only a subset of the relevant transition mechanisms and/or cannot accurately predict the sensitivity of those mechanisms to the changes in significant flow parameters. A preceding investigation had targeted the assessment of the transport-equation-based transition models in NASA's OVERFLOW 2.3b solver, namely, the amplification factor transport (AFT-2017b) equation model coupled with the Spalart-Allmaras RANS model and the Langtry-Menter transition models (LM2009 without crossflow effects and LM2015 including the modeling of crossflow transition) implemented with Menter’s shear-stress transport equation (SST2003) RANS model. Comparisons with recent measurements at transonic freestream conditions on the Common Research Model with Natural Laminar Flow (CRM-NLF) reinforced our earlier finding that all three of the above models significantly underpredict the reported extent of the laminar flow region over the entire span of the wing, regardless of the dominant instability mechanism(s) underlying the onset of the transition process. The underprediction of the laminar flow extent was attributed to the failure of the above models in accounting for the stabilizing effect of compressibility on the amplification of Tollmien-Schlichting instabilities. Based on previous linear stability studies related to compressibility effects, the present work proposes modifications to the two classes of transition models that reduce to the original form of each model at low subsonic speeds and do not require any nonlocal flow information or additional transport equation(s). The modifications are shown to significantly improve the predicted laminar extent of the flow and compare well against the data from the CRM-NLF experiment. Additionally, a previous assessment of transition prediction based on the dual, nonparallel N -factor method in conjunction with linear parabolized stability equations (PSE) is extended to additional angles of attack to provide the first comprehensive assessment of transition models based on nonparallel disturbance amplification over the CRM-NLF. In general, the transition criterion based on the dual, nonparallel N-factor method with N TS = N CF = 6 is reasonably successful at correlating with the measured transition fronts at R eMAC = 15 million for all angles of attack investigated herein and provides additional validation of the improved predictions from the compressibility-corrected transition models.

CFD modeling↗

NASA NTRS2022

Transition Analysis for the CRM-NLF Wind Tunnel Configuration using Transport Equation Models and Linear Stability Correlations

CFD modeling↗

NASA NTRSDate not supplied

Lunar Relay Onboard Navigation Performance and Effects on Lander Descent to Surface

A system of Lunar relay satellites has been proposed to address communication and navigation needs and ensure robustness for the variety of upcoming robotic and human exploration missions to the Moon. The relays are envisioned to estimate self position and time knowledge onboard, allowing the system to provide in-situ navigation services to missions in the Lunar and cis-Lunar environment. The quality and accuracy of those services are highly dependent on the navigation performance of the relay itself. To assess the Lunar relay navigation performance, a series of orbit determination (OD) Monte Carlo (MC) simulations are run using Lunar gravity modeling up to a degree and order of 250 and a variety of onboard clocks and measurement types including weak-signal GNSS, Ground Network (GN) pseudorange (PR) and Doppler, and optical navigation (OpNav) center-finding (CF). The estimated trajectories produced by these Lunar relay MC simulations, along with the associated errors, and transmitted navigation reference signal parameters, are used to evaluate the expected navigation performance of a user on a descent trajectory to the Lunar surface. The scenario features a lander system performing onboard navigation relying on one-way range and Doppler measurements from reference signals emitted by the Lunar relay. This paper can be used as a reference in determining the onboard clock and measurement types necessary to obtain acceptable navigation performance for the Lunar relay, and as a baseline for Lunar lander navigation performance using accurate measurements from a relay reference signal.

Jeffrey L. Small↗

NASA NTRSDate not supplied

Lunar Relay Onboard Navigation Performance and Effects on Lander Descent to Surface

A system of Lunar relay satellites has been proposed to address communication and navigation needs and ensure robustness for the variety of upcoming robotic and human exploration missions to the Moon. The relays are envisioned to estimate self-position and time knowledge onboard, allowing the system to provide in-situ navigation services to missions in the Lunar and cis-Lunar environment. The quality and accuracy of those services are highly dependent on the navigation performance of the relay itself. To assess the Lunar relay navigation performance, a series of orbit determination (OD) Monte Carlo (MC) simulations are run using Lunar gravity modeling up to a degree and order of 250 and a variety of onboard clocks and measurement types including weak-signal GNSS, Ground Network (GN) pseudorange (PR) and Doppler, and optical navigation (OpNav) center-finding (CF). The estimated trajectories produced by these Lunar relay MC simulations, along with the associated errors, and transmitted navigation reference signal parameters, are used to evaluate the expected navigation performance of a user on a descent trajectory to the Lunar surface. The scenario features a lander system performing onboard navigation relying on one-way range and Doppler measurements from reference signals emitted by the Lunar relay. This paper can be used as a reference in determining the onboard clock and measurement types necessary to obtain acceptable navigation performance for the Lunar relay, and as a baseline for Lunar lander navigation performance using accurate measurements from a relay reference signal.

Jeffrey L. Small↗

NASA NTRSDate not supplied

Highly Differentiated Basaltic Lavas Examined by PIXL in Jezero Crater

Textural, bulk chemical, and mineralogical data collected by PIXL(Planetary Instrument for X-ray Lithochemistry) indicate that the first rock unit (Cf-fr, Crater Floor-Fractured rough)examined by the M2020 Perseverance rover in Jezero crater is from a basaltic lava flow. This unit was originally mapped as volcanic flow[1]but has been reinterpreted as a clastic or volcaniclastic sediment[2].We here present evidence that it is a basalt flow, with implications for its petrogenesis as a highly differentiated basalt.

M E Schmidt↗

NASA NTRSDate not supplied

Topographic Trends of the Geologic Units in Jezero Crater: Lake Levels, Potential Shorelines, and the Crater Floor Units

The Mars2020 Perseverance rover continues to explore the crater floor of Jezero crater working to understand the origins of these different crater floor geological units. Here, we model and analyze the modern and paleo-topography of various facets of Jezero crater to better constrain the evolution and origins of the various units and features observed. In general, we find that that a) the evidences for lake levels are largely unconstrained and are primarily supported by the outflow channel, western delta, and Kodiak, b) so far no convincing evidence has been found for continuous lake terraces (i.e., shorelines), and c) the contact surface of the lobate Crater Floor Fractured Rough (Cf-fr)unit dips to the southeast.

S F Sholes↗

NASA NTRSDate not supplied

A Mars 2020 Perseverance SuperCam Perspective on the Igneous Nature of the Máaz Formation at Jezero Crater, Mars

The Mars2020 Perseverance rover landed in Jezero crater in February 2020,and first encountered the Máaz formation (or Crater floor-fractured rough(Cf-fr) unit previously mapped based on orbital data[1]).In this study, we use data from the SuperCam instrument(SCAM) to show that the Máaz formation has an igneous origin. SuperCam is a remote-sensing instrument onboard Perseverance, and investigates the texture, mineralogy, and chemistry of rocks and soils, as well as atmospheric features [2,3]. SuperCam comprises the Remote Micro Imager (RMI) to provide high-resolution images, laser-induced breakdown spectroscopy (LIBS)to investigate the major and minor element chemistry of materials, Raman, and passive visible-near infrared (VISIR) spectroscopy to analyze the mineralogy of targets, and time-resolved luminescence spectroscopy (TRLS)to measure organic fluorescence and inorganic luminescence. In addition, the microphone (MIC) can constrain the rock hardness through measurements of the shockwave of LIBS shots. In this study, we use LIBS-based major-oxide compositions (MOC),quantified using the calibration in [2].

Mars 2020↗

NASA NTRSDate not supplied

Air Quality Forecasting at Sub-City Scale by Combining Models, Satellites, and Surface Measures

While there are a variety of sources for air quality information, no one source simultaneously allows for high accuracy, low bias, fine spatial resolution, wide spatial coverage, high temporal frequency, and the capability for near-term forecasting of air quality. Global models, like the NASA’s Goddard Earth Observing System -Composition Forecasting (GEOS-CF) model, provide global coverage and forecasting capabilities, but operate at relatively coarse spatial resolution and require ground-truthing with in-situ data. Polar-orbiting satellite data products, like those of the ESA TROPOspheric Monitoring Instrument (TROPOMI), provide higher-spatial-resolution remote sensing of atmospheric composition, but are limited by cloud cover and overpass times and report column-integrated quantities. Surface measurements, both from regulatory-grade monitors and low-cost networks, measure “nose-level” air quality, but may not represent concentration variability across large spatial domains, and (in the case of low-cost sensors) are subject to interference and biases. There exists a great potential to combine these diverse data sources together, using the strengths of some to offset the weaknesses of others to build a more comprehensive picture of air quality. This presentation will summarize results from ongoing efforts to produce such a combined forecast, with application case studies for surface-level Nitrogen Dioxide forecasting in several major US cities. Furthermore, we will examine the relative impacts and benefits of different data sources on the forecasting accuracy at different spatial and temporal scales. Finally, we will examine the potential for integrating low-cost sensors into such a system, both in terms of using these integrated air quality estimates as a baseline from which to calibrate networks of low-cost sensors in the field, and in terms of using dense networks of low-cost sensors to refine the spatial resolution of integrated air quality forecasts.

Air Quality↗

NASA NTRSDate not supplied

Model Evaluation and Intercomparison Using Data Collected by the Langley Mobile Ozone Lidar in Hampton, Virginia

Throughout the year 2022, the Langley Mobile Ozone Lidar (LMOL) has been frequently collecting ozone and aerosol measurements in the lower troposphere. The lidar is located in the parking lot behind the Atmospheric Sciences building at NASA Langley in Hampton, Virginia, approximately 37.095 N, -76.389 W. The data was collected during a range of several distinct atmospheric conditions, including stratospheric intrusions, surface frontal passages, and long-range transported wildfire smoke plumes. We use this data to evaluate and intercompare the forecast accuracy of two atmospheric chemistry models: the GEOS Composition Forecasting model (GEOS-CF) and the Weather Research and Forecasting model with Chemistry (WRF-Chem). Both models make daily three-dimensional forecasts of many trace gases and species for the contiguous United States. Over a range of ten distinct collection events, each spanning 36 hours to nearly seven days, the forecast models predict tropospheric ozone at NASA Langley with reasonable accuracy. The models best predict the timing and shape of the observed stratospheric intrusions in the middle troposphere but often vary in the magnitude of the ozone mixing ratios. For other atmospheric conditions, there is more variability in the model accuracy. Here, we summarize the accuracy of the models for all events and investigate reasons for differences between the models and the lidar.

Daniel B. Phoenix↗

NASA NTRSDate not supplied

NASA TROPOMI Aerosol Products: Algorithmic Upgrades and Preliminary Evaluation

This poster presentation describes an expanded NASA TROPOMI (Tropospheric Monitoring Instrument)aerosol algorithm (N-TROPOMAER) that takes advantage of TROPOMI observations in the ultraviolet and visible spectral regions. The availability of the Oxygen B-band observations, and the unprecedentedly high spatial resolution (3.5 km X 5.5 km) for a hyper-spectral sensor are significant improvements for aerosol properties retrieval. The heritage N-TROPOMAER aerosol algorithm uses near-ultraviolet radiances at 354 nm and 388 nm from Sentinel 5 Precursor-TROPOMI for simultaneously retrieving aerosol optical depth (AOD), single-scattering albedo (SSA), aerosol absorption optical depth (AAOD), and above-cloud aerosol optical depth(ACAOD) at 388 nm, along with the qualitative UV aerosol index (UVAI). We have expanded the inversion capability beyond the UV, to retrieve AOD at 466 nm and 680 nm. Surface reflectance effects at466 nm are accounted for using a recently developed geometry-dependent surface Lambertian-equivalent reflectivity (GLER) product, which is derived from the top-of-atmosphere radiance computed with Rayleigh scattering and surface bidirectional reflectance distribution function (BRDF) for the exact viewing geometry at the sensor’s spatial resolution. Aerosol layer height (ALH) and 680 nm AOD are simultaneously derived from observations at 680 nm and at the Oxygen-B band (688 nm). Another important upgrade is the use of time averaged total column carbon monoxide from the NASA GEOS-CF(Global Earth Observing System Composition Forecast) as a tracer of carbonaceous aerosols.

TROPOMI↗

NASA NTRSDate not supplied

Towards A Flexible Data Fusion Tool Incorporating Model, Satellite, Regulatory Monitor and Low-Cost Sensor Data for Air Quality Estimation and Forecasting

Air quality managers, researchers, and concerned community scientists around the world have a variety of sources for air quality information, ranging from traditional regulatory monitoring networks and atmospheric chemistry models to remote sensing data products and low-cost sensor networks. However, the ability to incorporate data from these disparate sources and synthesize a comprehensive overview of the local air quality situation remains a considerable barrier for many end-users. This presentation will outline a tool, currently in development, which will address this need using a flexible data fusion approach. The tool will make use of air quality forecast model outputs (primarily from the NASA GEOS-CF composition forecast modeling system), satellite remote sensing data (from instruments including MODIS, VIIRS, TROPOMI, plus TEMPO for the US when available), and in-situ data from official regulatory and/or low-cost networks where these are available. The ability to incorporate data from low-cost sensor networks will be a key feature of the tool; it will make use of other available data sources to calibrate the low-cost sensor data on a regional scale, then use these calibrated low-cost sensor data for localized updating to resolve finer-scale air quality patterns. Development of this tool is taking place with the help of national and international partners and end-user groups, coordinated through the US EPA and the United Nations Environment Programme (UNEP). The tool is being developed on the Google Earth Engine cloud computing platform to facilitate integration of diverse data sources and free access by a broad community of end-users. Stewardship of the tool will be passed to US EPA and UNEP to support future activities with end-users in the US and around the world, and the tool itself will remain freely accessible. We hope that this tool will lower the barrier to entry for various user groups worldwide, including community scientists, who struggle to integrate disparate data sources to gain insight into their local air quality situations. This presentation will cover the early stages of the development of the tool, including the underlying methods and some pilot case studies in integrating low-cost sensor data.

global models↗

NASA NTRSDate not supplied

Analysis of Atmospheric Conditions Responsible for an Ozone Exceedance Event in Southeast Virginia on June 15, 2022

On June 15, 2022, the Virginia Department of Environmental Quality monitoring site at Suffolk/Holland, a rural site in southeast Virginia, recorded its first ozone exceedance since 2016. An ozone exceedance day occurs when the daily maximum 8-hour average surface ozone concentration is greater than 70 ppb. On this day, it was 75 ppb. This event is also noteworthy due to the rapid increase in surface ozone between 6 am and 9 am EDT as well as the hourly maximum ozone concentration of 82 ppb that was measured at 1 pm EDT. In this analysis, we utilize various observational and model data to determine the atmospheric conditions responsible for this ozone exceedance event. The analysis is conducted in two parts: (1) an evaluation of the accuracy of the GEOS-CF and WRF-Chem model forecasts and (2) an investigation of the mechanisms responsible for the high surface ozone at the Virginia DEQ Suffolk/Holland monitoring site. Comparisons of model forecasted ozone time-height cross sections with measured ozone by lidars from the Tropospheric Ozone Lidar Network (TOLNet) at NASA Langley and NASA Goddard reveal that both models forecasted ozone reasonably well near the surface. Model and observational data reveal that an upper-level ridge was present over much of the eastern United States, with a broad anticyclonic circulation near the surface, resulting in north-northeasterly flow over southeast Virginia. Back trajectory calculations using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) and model data suggest that a low-level plume rich in ozone and precursor species originating from the New York City and New Jersey region was transported along the east coast between June 14 and June 15, arriving in southeast Virginia around 6 am EDT on June 15. In the early morning hours, this plume mixed down to the surface, elevating the background ozone mixing ratio as well as the mixing ratios of several precursor species. Other potential contributions are also explored and discussed. Lastly, the authors note that this work represents the analysis of the authors and not the Virginia Department of Environmental Quality.

Daniel B Phoenix↗

NASA NTRSDate not supplied

Global Burden of Tropospheric Ozone: Effects of Particulate Nitrate Photolysis and Assimilation of NO2 Measurements

Tropospheric ozone is a major pollutant, a greenhouse gas, and the primary source of the hydroxyl radical. It is largely produced in situ from the oxidation of CO and volatile organic compounds in the presence of nitrogen oxides (NOx=NO+NO2). Global atmospheric chemistry models generally underestimate NOx and CO concentrations in the northern hemisphere away from source regions, which would lead to an underestimate in the tropospheric ozone burden. Here we evaluate the tropospheric ozone simulation in the GEOS-Chem model (version 14) using observations from satellite, aircraft, and ozonesondes. We include GEOS-Chem simulations conducted in the standard offline mode, as well as in the online mode within the NASA GEOS earth system model which forms the basis for the GEOS Composition Forecasts (GEOS-CF). In both configurations, GEOS-Chem underestimates ozone concentrations in the northern midlatitude free troposphere in spring and summer. We find that we can improve the ozone simulation by including an additional NOx source over the oceans from particulate nitrate photolysis on sea salt aerosols at rates inferred from field studies over the Atlantic Ocean. We investigate whether further improvements in the ozone simulation can be achieved by assimilating satellite measurements of NO2 from the Ozone Monitoring Instrument (OMI) and CO from the Measurement of Pollution in the Troposphere (MOPITT) instruments.

tropospheric ozone↗

NASA NTRSDate not supplied

Additional Metadata Guidelines to Improve the Structure and Usability of HDF and NetCDF Files

The Hierarchical Data Format (HDF) and Network Common Data Form (NetCDF) are data file formats created to aid users in the creation or use of scientific data. These file formats are useful for handling large data volumes and hosting extensive metadata as global attributes or variables and are popular with the modeling community. HDF and NetCDF files are largely used with remote sensing data and have been used to support measurements from numerous campaigns, from satellite to aircraft or ground and mobile based measurements. The files from airborne field studies, however, vary greatly in terms of the file structure and the amount and content of metadata. Information relevant to the file that can be useful to the user such as the data producer, location where data was taken, variable descriptions, or information about the instrument might not be included in the file. This metadata might be present in another file in the dataset containing the same data using the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) format. Recently, the Aerosols, Clouds, and their Interactions for Earth System Models (MACIE) group started a grassroots effort to develop a set of requirements for the HDF and NetCDF files for field studies, aiming to make the data products more interoperable and usable. Particularly, these requirements seek to make the files more compliant to Climate and Forecast (CF) metadata conventions and to standardize the file structure and the global and variable attributes. These requirements would help to ensure that HDF and NetCDF files contain adequate metadata to better support their use for research, e.g., the modeling community, and to enhance the usability and interoperability of data for research communities at large. To be presented are the details of the MACIE requirements as well as examples of the implementation of these requirements for merge files and lidar observation data files.

Sean Leavor↗

NASA NTRSDate not supplied

Transition Modeling Based on the Dual N-factor Method for the CRM-NLF Wind Tunnel Configuration

The dual N-factor method is used to model the boundary-layer transition over the common research model with natural laminar flow (CRM-NLF) aircraft configuration. The flow conditions match selected test conditions from a wind tunnel experiment in the National Transonic Facility at the NASA Langley Research Center. The paper presents a systematic methodology for transition prediction in the presence of a dual shock system and extends the prior capability for iteratively coupled computational fluid dynamics (CFD) predictions to incorporate three-dimensional, transonic wings. The method employs stability computations based on the linear parabolized stability equations (PSE), along with a dual N-factor criterion. The iterative process begins with the fully turbulent Reynolds-averaged-Navier-Stokes (RANS) mean flow solution. For the first iteration, a mean flow solution is calculated with an imposed transition front that aligns with the shock front from the fully turbulent solution. Subsequently, stability computations are performed along a set of streamlines across the wing to calculate the amplification of planar Tollmien-Schlichting (TS) and stationary crossflow (CF) modes. The transition criterion based on the dual N-factor method is used to infer the updated transition front and the process is successively repeated until convergence of the solution. Within three iterations, the predicted fronts for angles of attack of 1.45, 1.98, 2.46 and 2.94 degrees and a mean-aerodynamic-chord Reynolds number equal to 15 million, approach visual convergence in most regions of the studied cases, and the resulting predictions are in good agreement with the transition fronts deduced from measurements of temperature-sensitive paint. Even though surface pressure measurements based on fully-turbulent flow agree well with the measured pressure coefficient distributions, strong viscous-inviscid interaction effects cause significant shifts in the shock locations based on the imposed transition front, underscoring the intrusive nature of static pressure measurements using surface mounted ports on the CRM-NLF configuration.

Boundary Layer Transition↗

NASA NTRSDate not supplied

NASA GEOS Forecasting Capabilities for Air Quality

An overview of GEOS Forecasting capabilities for Air Quality, showing the evolution from GEOS with GOCART to GEOS with GEOS-Chem. Ways to access GEOS for research scientists and engaged community members will be given, as well as examples on how the GEOS-CF forecasts can be bias-corrected and downscaled for decision making processes.

K Emma Knowland↗

NASA NTRSDate not supplied

Coordinated Microstructural and Isotopic Characterization of Diamond Inclusions within the Kenna Ureilite

Introduction: The origin of diamonds in ureilitic meteorites, a major family of ultramafic achondrites, has long been debated, with various formation mechanisms inferred, including: 1) shock conversion of preexisting graphite due to a hypervelocity impact on the ureilite parent body [e.g., 1]; 2) vapor deposition during condensation of the solar nebula [e.g., 2]; or 3) static transformation in the mantle of a large planetesimal (>1000 km in diameter) [e.g., 3]. The Kenna ureilite is a 10.9 kg stone that was found in Kansas in 1972 and is dominantly composed of olivine and pigeonite (68% and 22% modal abundance, respectively), together with minor metal and carbon-rich regions. Within the carbon-rich domains researchers have identified microdiamonds up to ~10 µm in size [4]. Methods: Due to the proximity of the extremely hard microdiamonds with soft graphite and moderately hard silicates, achieving an appropriately flat surface for electron backscatter diffraction (EBSD) analysis has proven challenging. We have, therefore, employed a Hitachi ArBlade 5000 broad beam Ar ion milling system to prepare a sample with an adequately uniform surface flatness across the various minerals. The ion milling dramatically im-proved indexing of the EBSD patterns and facilitated high-resolution microstructural analysis of the carbon-rich domains. After microstructural EBSD analyses using an Oxford Symmetry detector we collected coordinated hy-perspectral cathodoluminescence (CL) analyses using a Gatan Monarc system mounted on a JEOL 7900F field emission (FE) scanning electron microscope at NASA Johnson Space Center. A focused ion beam (FIB) foil was extracted from a region of interest and analyzed using a JEOL 2500 FE scanning transmission electron microscope (STEM). In situ carbon and nitrogen isotopic analyses were collected from diamond-bearing domains using a Cameca NanoSIMS 50L at NASA JSC. Results: The carbon rich regions occur as tabular domains between polygonal olivine and pigeonite, and are composed of aggregates of micrometer-scale diamond with intermediate graphite, oxides and metal grains. Within the microdiamonds, {111} twins are abundant, many of which form ragged lamellae consistent with mechanical twinning. The microdiamond assemblages appear to pseudomorph tabular graphite habits, which together with the abundant deformation twins, support a shock transformation mechanism for microdiamond formation within the Kenna meteorite. Further supporting this observation, the diamond laths show close alignment of <111> and <110> between domains, consistent with a solid-state transformation from graphite along <0001> and <1-210>, respec-tively [5]. A FIB foil was extracted from a region with mechanical twins, and high resolution STEM analyses reveal cross-cutting veins of graphite and oriented kamacite crystals together with additional sets of {111} nano-twins formed in at least three orientations. Isotopic analyses of C and N reveal sector zoning patterns [cf. 3] that are unre-lated to the diamond substructure identified by EBSD yet consistent with hyperspectral CL images. Conclusions: These data provide new constraints on microdiamond formation within ureilites. The microdia-monds exhibit orientation variants defined by aligned <111> and <110> axes consistent with inherited crystallo-graphic orientations from transformation of a single hexagonal graphite parent crystal [5]. Crosscutting the micro-diamonds are veins of graphite and kamacite, together with additional {111} nanotwins. Taken together these data suggest that microdiamonds from ureilites formed from a solid state, diffusionless transformation from graphite, likely associated with a major shock event, which was subject to subsequent reversion either during decompression or a later deformation event. The N and C isotopic sector zoning is unrelated to diamond microstructures and likely reflects inheritance from the parent graphite crystal.

Ureilite↗