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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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335 records · Page 19

Selective and Stable In-Promoted Fe Catalyst for Syngas Conversion to Light Olefins

A selective and stable alumina-supported In-promoted Fe catalyst (10 mol % In based on Fe) was discovered for converting syngas (2:1 H 2 /CO ratio) to olefins with high selectivity (45% with CO 2 included) and a remarkable stability (72 h run) at a CO conversion of 10% at 400 °C and 5 bar. The X-ray photoelectron spectroscopy results indicated that incorporating In into Fe catalysts changed the chemical bonding state of Fe and Fe/In composition near the surface layers, which affected the catalytic reactivity. Steady-state isotopic transient kinetic analysis showed that more stable CHx fragments are present on the catalyst surface when incorporating In into Fe, which can promote C–C coupling reaction toward olefins. Our study demonstrates that introducing In into Fe catalysts on Al 2 O 3 support can lower CO activation abilities. It has more impacts on lowering hydrogenation activity, which permits more C–C couplings and like of “ene” hydrogenation. Furthermore, weakening CO adsorption both in terms of sites and strength encouraged CO toward hydrocarbons rather than CO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground‐Based Remote Sensing Observations in the Summer and Winter

Abstract The dairy industry in the San Joaquin Valley (SJV) is one of California’s largest methane (CH 4 ) sources. Reducing dairy emissions is a priority for the state’s climate change plans. Observations of current dairy CH 4 emissions are key to monitoring actions taken toward this goal. To help support this, we present new ground‐based measurements of atmospheric column‐averaged CH 4 mixing ratio (XCH 4 ) gradients across a group of 600 dairies in the central SJV using EM27/SUN solar spectrometers. We used measurements from the 2019 summer and 2020 winter seasons for a top‐down emission inversion based on the WRF‐STILT model. Our top‐down estimates of the region’s dairy emissions range from 90% to 183% of the current CALGEM inventory’s emissions of 277 Gg/yr. In contrast to the strong temperature dependence found by earlier dairy CH 4 emission studies, we also find that our top‐down emissions during the winter measurement days are comparable to the summer measurement days, possibly due to seasonal changes in dairy management practices and meteorological conditions. Furthermore, we find significant interday variability in our measurements and find that our emission estimates overlap with earlier top‐down studies and bottom‐up inventories in this region. Our study demonstrates how analysis of ground‐based remotely sensed CH 4 gradient observations can help improve our understanding of CH 4 sources at scales relevant to mitigation policy. It also reflects the need for long‐term monitoring of CH 4 emissions in the region and at individual facilities to better understand their emissions.

54 ENVIRONMENTAL SCIENCES↗

Spatial distributions of X CO 2 seasonal cycle amplitude and phase over northern high-latitude regions

Satellite-based observations of atmospheric carbon dioxide (CO 2 ) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO 2 (X CO 2 ) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed X CO 2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of X CO 2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5° latitude by 20° longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of X CO 2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of X CO 2 from the GEOS-Chem CO 2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72° N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of X CO2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest X CO 2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30d. The correlation of X CO 2 SCA with these land surface contact tracers is stronger than the correlation of X CO 2 SCA with the SCA of CO 2 fluxes or the total annual CO 2 flux within each 5° latitude by 20° longitude zone. This indicates that accumulation of terrestrial CO 2 flux during atmospheric transport is a major driver of regional variations in X CO 2 SCA.

54 ENVIRONMENTAL SCIENCES↗

Measurements of Line Positions and Strengths of HD O-18 and D2 O-18 in the 2500-4280 cm(exp -1) Region

Measurements of line positions and strengths of D2 O-18 and HD O-18 were obtained with a Fourier transform spectrometer. The data were analyzed to obtain energy levels of the (100), (020), and (001) vibrational states of HD O-18 and the (001) and (011) states of D2 O-18 and the vibrational bands of these states connected to the ground state covered the spectral region from 2500 to 4278 cm(exp -1). 456 absorption lines of D2 O-18 and 856 lines of HD O-18 were assigned from the spectra. The measurements were of oxygen-18 enriched samples of deuterated water vapor and the spectra also contained features of HD O-16, D2 O-16, H2 O-16, H2 O-17 and H2 O-18 of which several were used as frequency calibration standards.

Fourier transform spectrometer (FTS)↗

TES Level 1 Algorithms: Interferogram Processing, Geolocation, Radiometric, and Spectral Calibration

The Tropospheric Emission Spectrometer (TES) on the Earth Observing System (EOS) Aura satellite measures the infrared radiance emitted by the Earth's surface and atmosphere using Fourier transform spectrometry. The measured interferograms are converted into geolocated, calibrated radiance spectra by the L1 (Level 1) processing, and are the inputs to L2 (Level 2) retrievals of atmospheric parameters, such as vertical profiles of trace gas abundance. We describe the algorithmic components of TES Level 1 processing, giving examples of the intermediate results and diagnostics that are necessary for creating TES L1 products. An assessment of noise-equivalent spectral radiance levels and current systematic errors is provided. As an initial validation of our spectral radiances, TES data are compared to the Atmospheric Infrared Sounder (AIRS) (on EOS Aqua), after accounting for spectral resolution differences by applying the AIRS spectral response function to the TES spectra. For the TES L1 nadir data products currently available, the agreement with AIRS is 1 K or better.

infrared remote sensing↗