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Results for “ATMOSPHERIC COMPOSITION”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Regolith Evolved Gas Analyzer (REGA): An Instrument to Characterize the Martian Soil Mineralogy and Atmosphere Composition

This abstract describes an instrument and experiment to be proposed for a future Mars surface mission to conduct basic research on environmental characterization. The Regolith Evolved Gas Analyzer (REGA) experiment is designed to provide information on Mars surface material properties in preparation for human missions of exploration. The goals of the investigation are: 1) Define and determine surface mineralogy of soil and dust and their effects on humans and machines; and 2) Conduct in-situ investigations aimed at identifying possible evidence of past or present life on Mars.

Hoffman, John H.↗
NASA NTRS2007

Risk Assessment of Physiological Effects of Atmospheric Composition and Pressure in Constellation Vehicles

To limit the risk of fire and reduce denitrogenation time to prevent decompression sickness to support frequent extravehicular activities on the Moon, a hypobaric (PB = 414 mmHg) and mildly hypoxic (ppO2 = 132 mmHg, 32% O2 - 68% N2) living environment is considered for the Crew Exploration Vehicle (CEV) and Lunar Surface Access Module (LSAM). With acute change in ppO2 from 145-178 mmHg at standard vehicular operating pressure to less than 125 mmHg at desired lunar surface vehicular operating pressures, there is the possibility that some crewmembers may develop symptoms of Acute Mountain Sickness (AMS). The signs and symptoms of AMS (headache plus nausea, dizziness, fatigue, or sleeplessness), could impact crew health and performance on lunar surface missions. An exhaustive literature review on the topic of the physiological effects of reduced ppO2 and absolute pressure as may contribute to the development of altitude symptoms or AMS was performed. The results of the nine most rigorous studies were collated, analyzed and contents on AMS and hypoxia symptoms summarized. There is evidence for an absolute pressure effect per se on AMS, so the higher the altitude for a given hypoxic alveolar O2 partial pressure (PAO2), the greater the AMS response. About 25% of adults are likely to experience mild AMS near 2,000 m altitude following a rapid ascent from sea level while breathing air (6,500 feet, acute PAO2 = 75 mmHg). The operational experience with the Shuttle staged denitrogenation protocol at 528 mmHg (3,048 m) while breathing 26.5% O2 (acute PAO2 = 85 mmHg) in astronauts adapting to microgravity suggests a similar likely experience in the proposed CEV environment. We believe the risk of mild AMS is greater given a PAO2 of 77 mmHg at 4,876 m altitude while breathing 32% O2 than at 1,828 m altitude while breathing 21% O2. Only susceptible astronauts would develop mild and transient AMS with prolonged exposure to 414 mmHg (4,876 m) while breathing 32% O2 (acute PAO2 = 77 mmHg). So the following may be employed for operational risk reduction: 1) develop procedures to increase PB as needed in the CEV, and use a gradual or staged reduction in cabin pressure during lunar outbound; 2) train crews for symptoms of hypoxia, to allow early recognition and consider pre-adaptation of crews to a hypoxic environment prior to launch, 3) consider prophylactic acetazolamide for acute pressure changes and be prepared to treat any AMS associated symptoms early with both carbonic anhydrase inhibitors and supplemental oxygen.

Scheuring, Richard A.↗
NASA NTRS2015

Spatial Variability of Trace Gases During DISCOVER-AQ: Planning for Geostationary Observations of Atmospheric Composition

Results from an in-depth analysis of trace gas variability in MD indicated that the variability in this region was large enough to be observable by a TEMPO-like instrument. The variability observed in MD is relatively similar to the other three campaigns with a few exceptions: CO variability in CA was much higher than in the other regions; HCHO variability in CA and CO was much lower; MD showed the lowest variability in NO2All model simulations do a reasonable job simulating O3 variability. For CO, the CACO simulations largely under over estimate the variability in the observations. The variability in HCHO is underestimated for every campaign. NO2 variability is slightly overestimated in MD, more so in CO. The TX simulation underestimates the variability in each trace gas. This is most likely due to missing emissions sources (C. Loughner, manuscript in preparation).Future Work: Where reasonable, we will use these model outputs to further explore the resolvability from space of these key trace gases using analyses of tropospheric column amounts relative to satellite precision requirements, similar to Follette-Cook et al. (2015).

trace gas↗
NASA NTRS2016

MUlti-SpEctral, MUlti-SpEcies, MUlti-SatEllite (MUSES) Retrieval Algorithm: Towards Extending Multi-Decadal NASA EOS Atmospheric Composition Data Records

Multi-Spectra, Multi-Species, Multi-Sensors (MUSES): Builds off of heritage from the Tropospheric Emission Spectrometer (TES) optimal estimation (OE) algorithm to combine a priori and satellite data, including rigorous error analysis diagnostics and observation operators needed for trend analysis, climate model evaluation, and data assimilation; has generic design to incorporate forward model radiances from hyperspectral measurements from multiple sensors into the joint retrieval algorithm.

Fu, Dejian↗
NASA NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗