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NASA NTRS2003

TRMM Fire Algorithm, Product and Applications

Land fires are frequent menaces to human lives and property. They also change the state of the vegetation and contribute to the climate forcing by releasing large amount of aerosols and greenhouse gases into the atmosphere. This paper summarizes methodologies of detecting global land fires from the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Scanner FIRS) measurements. The TRMM Science Data and Information System (TSDIS) fire products include global images of daily hot spots and monthly fire counts at 0.5 deg. x 0.5 deg. resolution, as well as text fiies that details necessary information of all fire pixels. The information includes date, orbit number, pixel number, local time, solar zenith angle, latitude, longitude, reflectance of visible/near infrared channels, brightness temperatures of infrared channels, as well as background brightness temperatures of infrared channels. These products have been archived since January 1998. The TSDIS fire products are compared with the coincidental European Commission (EC) Joint Research Center (JRC) 1 km AVHRR fire products. Analyses of the TSDIS monthly fire products during the period from 1998 to 2003 manifested seasonal cycles of biomass fires over Southeast Asia, Africa, North America and South America. The data also showed interannual variations associated with the 98/99 ENS0 cycle in Central America and the Indonesian region. In order to understand the variability of global land fires and their effects on the distribution of atmospheric aerosols, statistical methods were applied to the TSDIS fire products as well as to the Total Ozone Mapping Spectrometer (TOMS) aerosol index products for a period of five years from January 1998 to December 2002. The variability of global atmospheric aerosol is consistent with the fire variations over these regions during this period. The correlation between fire count and TOMS aerosol index is about 0.55 for fire pixels in Southeast Asia, Indonesia, and Africa. Parallel statistical analyses such as Empirical Orthogonal Function (EOF) analysis and Singular Spectrum Analysis (SSA) methods were applied to pentad TRMM fire data and TOMS aerosol data. The EOF analyses showed contrast between North and South hemispheres and also inter- continental transitions in Africa and America. EOF and SSA analyses also identified 25-60 day intra-seasonal oscillations that were superimposed on the annual cycles of both fire and aerosol data. The intra-seasonal variability of fires showed similarity of tropical rainfall oscillation modes. The TRMM fire products were also compared to the coincident TRMh4 rainfall and other rainfall products to investigate the interaction between rainfall and fire. The results indicate that the annual, interannual and intraseasonal variability of fire are dominated by global rainfall variations. However, the feedback of fire to the rainfall occurrence at regional scale for certain regions is also evident.

Ji, Yi-Min↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRS1994

HRMA calibration handbook: EKC gravity compensated XRCF models

This document, consisting of hardcopy printout of explanatory text, figures, and tables, represents one incarnation of the AXAF high resolution mirror assembly (HRMA) Calibration Handbook. However, as we have envisioned it, the handbook also consists of electronic versions of this hardcopy printout (in the form of postscript files), the individual scripts which produced the various figures and the associated input data, the model raytrace files, and all scripts, parameter files, and input data necessary to generate the raytraces. These data are all available electronically as either ASCII or FITS files. The handbook is intended to be a living document and will be updated as new information and/or fabrication data on the HRMA are obtained, or when the need for additional results are indicated. The SAO Mission Support Team (MST) is developing a high fidelity HRMA model, consisting of analytical and numerical calculations, computer software, and databases of fundamental physical constants, laboratory measurements, configuration data, finite element models, AXAF assembly data, and so on. This model serves as the basis for the simulations presented in the handbook. The 'core' of the model is the raytrace package OSAC, which we have substantially modified and now refer to as SAOsac. One major structural modification to the software has been to utilize the UNIX binary pipe data transport mechanism for passing rays between program modules. This change has made it possible to simulate rays which are distributed randomly over the entrance aperture of the telescope. It has also resulted in a highly efficient system for tracing large numbers of rays. In one application to date (the analysis of VETA-I ring focus data) we have employed 2 x 10(exp 7) rays, a substantial improvement over the limit of 1 x 10(exp 4) rays in the original OSAC module. A second major modification is the manner in which SAOsac incorporates low spatial frequency surface errors into the geometric raytrace. The original OSAC included the ability to use Legendre-Fourier polynomials to describe deviations from the basic optical prescription. To this we have added bicubic splines to address a deficiency in the handling of the sharper deformations in the areas of mirror support pads. SAO has developed software (TRANS-FIT) to translate the most common finite element analysis models into these forms for incorporation into the raytrace program.

Tananbaum, H. D.↗
NASA NTRS2003

Rough-Wall Channel Analysis Using Suboptimal Control Theory

The original aim of this work was to shed some light on the physics of turbulence over rough walls using large-eddy simulations and the suboptimal-control wall boundary conditions introduced by Nicoud et al. It was hoped that, if that algorithm was used to fit the mean velocity profile of the simulations to that of a rough-walled channel, instead of to a smooth one, the wall stresses introduced by the control algorithm would give some indication of what aspects of rough walls are most responsible for the modification of the flow in real turbulence. It was similarly expected that the structure of the resulting velocity fluctuations would share some of the characteristics of rough-walled flows, thus again suggesting what is intrinsic and what is accidental in the effect of geometric wall roughness. A secondary goal was to study the effect of 'unphysical' boundary conditions on the outside flow by observing how a relatively major change of the target velocity profile, and therefore presumably of the applied wall stresses, modifies properties such as the dominant length scales of the velocity fluctuations away from the wall. As will be seen below, this secondary goal grew more important during the course of the study, which was carried out during a short summer visit of the first two authors to the CTR. It became clear that there are open questions about the way in which the control algorithm models the boundary conditions, even for smooth walls, and that these questions make the physical interpretation of the results difficult. Considerable more work in that area seems to be needed before even relatively advanced large-eddy simulations, such as these, can be used to draw conclusions about the physics of wall-bounded turbulent flows. The numerical method is the same as in Nicoud et al. The modifications introduced in the original code are briefly described in section 2, but the original paper should be consulted for a full description of the algorithm. The results are presented in section 3 and summarized in section 4. The elementary properties of turbulence over rough walls which are used in the text have been taken from recent reviews such as Raupach et al. or Jimenez.

Flores, O.↗
NASA NTRS2023

SafeAeroBERT: Towards a Safety-Informed Aerospace-Specific Language Model

As aviation systems continue to operate with high traffic, large amounts of documents containing safety-relevant data continue to be generated via reporting systems such as the ASRS. Advanced natural language processing techniques, specifically pre-trained language models, have shown great success in domain-specific applications; however, the text in aviation safety reports is inundated with jargon and thus not fully utilized by general pre-trained models. In this research, we work towards developing a safety-informed aerospace-specific language model by pre-training a Bidirectional Encoder Representations from Transformer (BERT) model on reports from the Aviation Safety Reporting System and the National Transportation Safety Board. The resulting model, called SafeAeroBERT, is fine-tuned for the specific task of document classification, and can be further tuned for named-entity recognition, relation detection, information retrieval, and summarization. Results from the classification task are compared between SafeAeroBERT, the base BERT, and SciBERT models and show SafeAeroBERT outperforms the general BERT and SciBERT on classifying reports about human factors, aircraft, and procedure. SafeAeroBERT can be used on custom tasks, not limited to document classification, and is intended to aid an intelligent knowledge manager for safety report repositories.

Aviation↗
NASA NTRS2018

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗
NASA NTRS2016

GeneLab Phase 2: Integrated Search Data Federation of Space Biology Experimental Data

The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.

Space Biology↗
NASA NTRS2005

Introduction to Flight Test Engineering [Introduction Aux Techniques Des Essais En Vol]

Flight test is at the core of what organizations must do in order to validate the operation and systems on an aircraft. While the AGARDograph series 300 and 160 series deal with aspects of this testing, this volume pulls it all together as an introduction to the process required to do effective flight test engineering. This volume was originally published in 1995. Its utility has been proven in that many flight test organizations and universities have requested copies for their engineers and students. It was felt that re-issuing it in a new format designed for electronic publication would be valuable to the community. This second printing changes none of the text, but rather reformats it. All the original references to AGARD (instead of RTO) are left in place so that none of the flavor of the original publication is lost. This is the Introductory Volume to the Flight Test Techniques Series. It is a general introduction to the various activities and aspects of Flight Test Engineering that must be considered when planning, conducting, and reporting a flight test program. Its main intent is to provide a broad overview to the novice engineer or to other people who have a need to interface with specialists within the flight test community. The first two Sections provide some insight into the question of why flight test and give a short history of flight test engineering. Sections 3 through 10 deal with the preparation for flight testing. They provide guidance on the preliminary factors that must be considered; the composition of the test team; the logistic support requirements; the instrumentation and data processing requirements; the flight test plan; the associated preliminary ground tests; and last, but by no means least, discuss safety aspects. Sections 11 through 27 describe the various types of flight tests that are usually conducted during the development and certification of a new or modified aircraft type. Each Section offers a brief introduction to the topic under consideration, and the nature and the objectives of the tests to be conducted. It lists the test instrumentation (and, where appropriate, other test equipment and facilities) required, describes the test maneuvers to be executed, and indicates the way in which the test data is selected, analyzed, and presented. The various activities that should take place between test flights are presented next. Items that are covered are: who to debrief; what type of reports to send where: types of data analysis required for next flight; review of test data to make a comparison to predicted data and some courses of action if there is not good agreement; and comments on selecting the next test flight. The activities that must take place upon completion of the test program are presented. The types of reports and briefings that should take place and a discussion of some of the uses of the flight test data are covered. A brief forecast is presented of where present trends may be leading.

Test facilities↗
NASA NTRS2013

ERRATUM: FERMI Large Area Telescope Study of Cosmic-Rays and the Interstellar Medium in Nearby Molecular Clouds

In the published version of the paper, errors were made in calculating the exposure time due to an analysis mistake. While they do not affect gas emissivities of the R CrA and Cepheus & Polaris flare regions significantly (the differences are within the systematic uncertainty), that of the Chamaeleon region is increased by approx.20%. Although we claimed a difference of ∼50% in gas emissivity among these molecular cloud regions in the original paper, it is decreased to ∼30% (comparable to the sum of the statistical and systematic uncertainties) in the revised analysis. Therefore, our conclusion of the original paper, that a small variation (approx. 20%) of the CR density in the solar neighborhood exists, is not supported by the data if we take these uncertainties into account. On the other hand, the obtained XCO and XAv values, and the masses of gas calculated from them are not changed significantly (the differences are within the statistical errors). Errors and corrections in the original paper are summarized below. 1. In the Abstract (lines 5-6) and Section 3 (lines 4-5 in the 3rd paragraph) in the original paper, the gamma -ray emissivity above 250 MeV for the Chamaeleon region should be (7.2 +/- 0.1stat +/- 1.0sys) × 10(exp −27) photons/s/sr/H-atom, not (5.9 +/-0.1stat +0.9−1.0sys) × 10(exp −27) photons/s/sr/H-atom. 2. In the Abstract (lines 8-10), "Whereas the energy dependences of the emissivities agree well with that predicted from direct CR observations at the Earth, the measured emissivities from 250 MeV to 10 GeV indicate a variation of the CR density by approx.20% in the neighborhood of the solar system, even if we consider the systematic uncertainties." should be changed to "The energy dependences of the emissivities agree well with that predicted from direct CR observations at the Earth. Although the measured emissivities from 250 MeV to 10 GeV differ by approx.30% among these molecular cloud regions, the difference is not significant if we take the systematic uncertainty into account." 3. Table 1 and Figure 13, which show gas emissivities and spectra for the Chamaeleon region in the original paper, should be changed to the Table 1 and Figure 1 as shown below. 4. Figure 16, which compares Hi gas emissivities among several regions in the original paper, should be changed to Figure 2 as shown below. 5. The text from the line 13 to the last one in the first paragraph of Section 4.1, "The spectral shapes for the three regions..., indicating a difference of the CR density between the Chamaeleon and the others as shown in Figure 16." should be changed to the paragraph that follows. "The shaded area of each spectrum indicates the systematic uncertainty as described in Section 3. We note that the systematic uncertainty of the LAT effective area (5% at 100 MeV and 20% at 10 GeV; Rando et al. 2009) does not affect the relative value of emissivities. The effect of unresolved point sources is small; we have verified that the obtained emissivities are almost unaffected by decreasing the threshold for point sources from TS = 100 to TS = 50. We also confirmed that the residual excess of photons around (l = 280deg to 288deg, b = −20deg to −12deg; see the bottom panel of Figure 8) in the Chamaeleon region does not affect the local Hi emissivity very much. Thus the total systematic uncertainty is reasonably expressed by the shaded area shown in Fig. 1.

emissivities↗
NASA NTRS1998

Structure and Dynamics of Coronal Plasma

Brief summaries of the four published papers produced within the present performance period of NASA Grant NAGW-4081 are presented. The full text of the papers are appended to the report. The first paper titled "Coronal Structures Observed in X-rays and H-alpa Structures" was published in the Kofu Symposium proceedings. The study analyzes cool and hot behavior of two x-ray events, a small flare and a surge. It was found that a large H-alpha surge appears in x-rays as a very weak event, while a weak H-alpha feature corresponds to the brightest x-ray emission on the disk at the time of the observation. Calculations of the heating necessary to produce these signatures, and implications for the driving and heating mechanisms of flares vs. surges are presented. The second paper "Differential Magnetic Field Shear in an Active Region" has been published in The Astrophysical Journal. The study compared the three dimensional extrapolation of magnetic fields with the observed coronal structure in an active region. Based on the fit between observed coronal structure throughout the volume of the region and the calculated magnetic field configurations, the authors propose a differential magnetic field shear model for this active region. The decreasing field shear in the outer portions of the AR may indicate a continual relaxation of the magnetic field with time, corresponding to a net transport of helicity outward. The third paper "Difficulties in Observing Coronal Structure" has been published in the journal Solar Physics. This paper discusses the evidence that the temperature and density structure of the corona are far more complicated than had previously been thought. The discussion is based on five studies carried out by the group on coronal plasma properties, showing that any one x-ray instrument does see all of the plasma present in the corona, that hot and cool material may appear to be co-spatial at a given location in the corona, and that simple magnetic field extrapolations provide only a poor fit to the observed structure. The fourth paper "Analysis and Comparison of Loop Structures Imaged with NIXT and Yohkoh/SXT" has been published in Astronomy and Astrophysics. This paper analyzes and compares a variety of coronal loops, deriving loop pressure and emission measure from loop models. They are able to determine the volume filling factor in the corona, which is found to be in the range 0.001 to 0.01 for compact loops, and of order 1 for large structures. The small values suggest highly filamented structures, especially at lower temperatures.

Golub, Leon↗
NASA NTRSDate not supplied

Examining Weathering of Magnesite in an Arid Environment: Implications For Jezero Crater

Introduction:Orbiter data indicatethe presence of carbonates in severallocations on the surface of Mars[1],but Jezero crater, landing site of the Perseverancerover,is the only known location where carbonatesap-pear coincident with evidence of fluvialand lacustrineactivity [2].On Earth, carbonates in close proximity to these paleoenvironments mayindicatethe presence of past microbial life,like stromatolites[3], that could re-sult inbiosignatures [2]. However,in other cases,car-bonates can also form throughthe alteration of mafic materialwiththe introductionof carbonic acid[4].Hy-drated magnesites have also been found in evaporative environments along lake shores, and in playas[5,6,7].Correctly interpreting past carbonates on Mars is there-fore critical in the search for past signs of life. In Jezero crater,both thenorthernand western fans haveMg-rich carbonates intermixed with olivine-rich material[8].According to CRISM data, magnesite(MgCO3), along with hydromagnesite(Mg5(CO3)4(OH)2•4H2O), arepotential candidatesfor these Mg-carbonates [2]. Considering the spatial con-text with olivine,there aremultiplepotential explana-tions for the presence ofMg-carbonatesin this locationincludingin-situformation via alterationof olivine-rich materialwith carbonic acid,transportationfrom farther up in the watershed, or precipitation of lacustrine car-bonates[2]. The formation of hydromagnesite rather than magnesite is favored when Mg2+saturated solutions have a high CO32-/HCO3-ratio, which, on Earth, is thought to be caused byinflow of groundwater [4]. Additionally, Mg-carbonates tend to precipitate under high pH condi-tions and are unstable at lower pH conditions [5]. Hy-dromagnesite is stable at atmospheric CO2pressure and temperature conditions common to most Earth surface environments [9]. However, it is subject to transfor-mation to magnesite after dehydration and concomitant brucite formation or dissolution and reprecipitation [10].Previousresearch suggests that hydrated car-bonates, including hydromagnesite, can formas weath-ering productsof mafic minerals in the presenceof H2O and CO2in subfreezing temperatures and would not de-hydrate under Martian atmospheric conditions [11,12].It is critical to understand the formation conditions of Mg-carbonatesbecause of the different implications for the past history of Martian environments. Therefore, in this work we are investigating the weathering of Mg-carbonatesin arid environments to helpbetter understand Mg-carbonates in Jezero crater. Study Area:The Ala-Mar Mines(East and West)near Ely, NVare the site ofmultiple magnesitedepositsfound within a calcareous tuff formationthat overlies Tertiary aged volcanic rocks.Here,magnesiteis formed via the alteration of the calcareous tuff and occurs innodules, veins,and lenses[13]. Previous work suggests magnesite deposits are associated with faults [13]. Within the West Mine, magnesite can be found in two maincontexts: (1) relatively circular zones of cauli-flower-like material found within (2) a more massivelensthat is heavily fractured on the surface.Methods.Samplesof both the cauliflower texture and more massive materialwere collectedat Ala Mar West Mine. Both samples were thenpowdered, sieved and analyzed with an inXitu Terra Portable XRD. The program QualX was used to identify potential mineral phases [14].Both samples were also optically inspected using 10x and 20x hand lenses.Figure 1. XRD patterns for the cauliflower magnesite (top) and massive magnesite (bottom). Ongoing and future work on the samples discussed above includes scanning electron microscopy (SEM), electron microprobe analysis (EMPA), and near-infra-red spectroscopy to determine whether hydromagnesite is present. Separation and analysis of the clay-size frac-tionby XRD will helpto better identify any phyllosili-cate phases present. Results and Discussion:Both textures are a white to light tan with a porcelain luster on weathered sur-faces, along with minor iron staining in some areas. Likewise, both textures are white with a porcelain luster on fresh surfaces. When broken apart, the massive mag-nesite shows macroscopic crystals, unlike the cauli-flower magnesite. XRD analysis shows that both samples have high concentrationsof magnesite with lesser amounts of thecarbonatemineral huntite(Mg3Ca(CO3)4; Figure1).The more massive samplecontainsa serpentine-groupmineral,with lizardite being apotential candidate. The cauliflower sample has several minor peaks that may correspond to hydromagnesite(Figure 1), although more work is needed to confirm this.Additionally, thecauliflower deposits closely resemble hydromagnesite deposits found in southwestern Turkey, formed via mi-crobialites[15].As such, it is likely that moreaqueous alterationor weatheringis occurring at the locations where the cauliflower magnesite is present. However, additional field work will need to be conducted to con-firm this hypothesis. Conclusions and Future Work:Future work will include field mapping of fault locations andadditional samplingof the different magnesite types as well as of the calcareous tuffmaterial.We will also look specifi-cally for potential weathering products of magnesite in this arid location, which may yield important insight into the Mg-carbonates located in Jezero crater. XRD analyses on aPANalytical XRDusing non-ambient stages will be used to investigate the stability of hydro-magnesiteat different humiditiesand temperatures, which has implications for samples to bereturned to Earth in the future. Additionally, thermal and evolved gas analysis of magnesite and hydromagnesite will be compared to results from Gale Craterto help interpret the mineralogy inthat location[16]. The results of this research will further ourunderstanding of carbonate for-mationin volcanic settingsandtheirweathering pro-cessesin arid environments. Acknowledgments:We acknowledge funding for this research from Jacobs Technology at the Johnson Space Center.We would also like to thank Ngoc Luu, Christopher Adcock, Richard Allanson, and the rest of the UNLV Soil Science Teamfor their continued sup-portwith troubleshooting and otherlab work. References:[1] Ehlmann, B.L., and Edwards, C.S. (2014) Annual Review of Earth and Planetary Sci., 42, 291–315. [2] Horgan, B.H.N., et al. (2020) Icarus, 339, 113526. [3] Bosak, T., et al. (2013) Annual Review of Earth and Planetary Sci, 41, 21–44. [4] Pohl, W.L. (1989) Gebriider Borntraege, 28, 1-13. [5] Müller, G., et al. (1972) Die Naturwissenschaften, 59, 158–164. [6] Walter, M.R., et al. (1973) Journal of Sedimentary Pe-trology, 43, 1021–1030. [7] Braithwaite, C.J.R., and Zedef, V. (1994) Sedimentary Geology, 92, 1–5. [8] Goudge, T.A., et al. (2015) JGR: Planets, 120, 775–808. [9] Langmuir, D. (1965) Journal of Geology, 73, 730–754. [10] Zhang, P., et al. (2000) Applied Geo-chem., 286, 1748–1753. [11] Calvin, W.M., et al. (1994) JGR, 99, 14659-14675. [12]Russell, M.J., et al. (1999) Journal of the Geological Society of London, v. 156, p. 869–888. [13] Faust, G.T., and Callaghan, E. (1948) GSA Bulletin, 59, 11–74. [14] Altomare, A., et al. (2015) J. of Applied Crystallography, 48, 598–603. [15] Zedef, V.,et al. (2000) Economic Geology, 95, 429–445. [16] Leshin, L.A. et al., (2013) Science, 341, 1–9

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In-Lab Rapid Analytical Detection of Lunar Volatiles By Universal Gas Analyzer With Comparison to GC-MS System

Introduction: The curation of permanently shadowed regions (PSRs) [1] on the lunar surface is centered around studies based upon the observed volatiles from the LCROSS mission [2]. The rapid detection of important volatile gases and vapors present in planetary bodies and Astromaterials by a standalone analytical device is an area of intense research interest in our group and Planetary Exploration & Astromaterials Research Laboratory (PEARL) facility and this work is relevant to the future preparation of viable lunar simulants for testing curation efforts down the road. The groundbreaking results obtained from the LCROSS Mission [2] open the requirements for the direct detection of volatiles present in regolith materials collected from the lunar surface. The mass spectrometry of volatile chemicals is a general technique that utilizes a set of instruments that creates charged ions from a gaseous chemical species and measures the intensities vs. mass-to-charge ratio (m/z) [3]. In this context, we discuss in-lab experimental results and procedures for rapid qualitative analysis of main LCROSS volatiles (water, H2S, NH3, CO2, and CH3OH) by a Universal Gas Analyzer (UGA) instrument. Additionally, the instrument performance was evaluated by measuring the isotopic abundance ratio of atmospheric Ar-40 to Ar-36 present in room air since, argon is a relevant gas in planetary studies as it can provide an insight and reference point to isotope studies [4]. Additional, cross comparisons were attempted and made between the two instruments to develop a robust analytical technique by comparing mass spectral data for H2S headspace samples with a Trace-1310/ ISQ 7000 (ThermoFisher Scientific.) GC-MS system. Background: The benchtop UGA System is equipped with an SRS UGA 300 quadrupole mass spectrometer designed and built by Stanford Research Systems [5]. This system can be configured for several types of gaseous chemical analysis. The inlet line continuously samples gases at low flow rates (several milliliters per minute) through a capillary limiting the intake pressure making the instrument ideal for online analysis of select gases and/or room atmosphere. Moreover, in our current UGA system, a change in composition at the inlet can be detected in about 200 milliseconds and a complete spectrum is acquired (for a range of 1-100 amu) in under 45 sec with masses measured at rates up to 25 msec per point [5]. This system provides a quick upstream analytical data that we can then compare to results obtained by our GC-MS system. Sample Preparation: Small volume (2-4 mL) of analyte sample was taken in a 10 mL glass vial and sealed with a crimped cap and purged with pure Ar or N2 gas to displace air from the top. The headspace sample was scanned by the UGA instrument at analog, histogram, and pressure vs. time modes. The isotopic abundance ratio for 40Ar-to-36Ar was estimated by measuring partial pressure vs. time scans and setting the mass at 40 and 36 respectively. Results and Discussions: In this work, we have investigated the applicability of the UGA system by qualitative analysis of a series of LCROSS volatiles measured individually. Fig. 1 demonstrates a set of vertically offset spectra for the partial pressures measured as a function of mass-to-charge (m/z) ratios. The average acquisition time for each spectrum was less than a minute suggesting that the UGA system is ideal for quick analysis of geochemical volatiles. For the cross-comparison, we analyzed an H2S headspace sample by a Trace-1310/ISQ-7000 system and compared mass spectral data with previously measured UGA histogram scan data (Fig. 2). In both cases, major peak positions are the same, however, the intensities of fragment ions ([1H132S]+ and [32S]+) are higher for UGA suggesting that the fragment ionization process is stronger in UGA compared to that of GC-MS. To investigate how the integrated area under each chromatogram varies with the headspace sample volume, a set of five H2S headspace samples with increasing volumes was analyzed by the GC-MS system (Fig. 3, inset). A small volume (e.g., 200 to 1000 µL) of H2S/H2O vapor was withdrawn from a 20 mL stock sample vial containing ~5 mL of 0.4% H2S in water by a gas-tight syringe and added to another 20 mL vial filled with argon and analyzed by the GC-MS system. Finally, the UGA detector sensitivity was evaluated by calculating the atmospheric 40Ar-to-36Ar isotopic abundance ratio in room air by running a partial pressure vs. time scan with setting the atomic mass at 40, and 36. Fig. 4(a) shows a ~25 min duration “P vs. time” scan for 40Ar (plot for 36Ar is not shown). The partial pressure values (~100 points) were corrected by subtracting the corresponding background pressure value for 37Ar and utilized to calculate 40Ar-to-36Ar isotopic abundance ratios as shown by Fig. 4b. The average isotopic abundance ratio is ~306 with a 2*STDEV ~13. This abundance ratio is significantly close to the previously reported value of 298.56 [6] and the ratio obtained by our GC-MS system (303 for a UHP grade Ar sample). Conclusions: Our study strongly evidenced that the benchtop UGA system is a valuable analytical tool for the detection of major LCROSS volatiles. The rapid scanning capability, the inexpensiveness of the whole system, and impressive detection sensitivity prove its worthiness as an essential device for advanced geochemical applications. Moreover, cross comparisons with the GC-MS provide important bridges into advanced curatorial efforts into the future. References: [1] Bickel, V.T., et al. (2021) Nat Commun 12, 5607. [2] Colaprete, A., et al. (2010) Science, 330, 463-468. [3] Glavin, D. P. et al. (2012) 2012 IEEE Aerospace Conference, 1-11. [4] Willett, C. D., et al. (2022) Geochimica et Cosmochimica Acta 329, 119-134. [5] Operation Manual and Programming Reference. (2018) Universal gas Analyzers, Stanford Research Systems. [6] Lee, J. Y., et al. (2006) Geochimica et Cosmochimica Acta 70, 4507–4512. Notes: (4 figures are attached with text as shown by the attached file)

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