NACHOS: An ultra-compact CubeSat-based UV-Visible hyperspectral trace gas sensor
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Earth Sciences↗
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NACHOS: An ultra-compact CubeSat-based UV-Visible hyperspectral trace gas sensor
Explore the source record for details and available documents.
Earth Sciences↗
Gas-mediated trace element incorporation into rhyolite-hosted topaz: A synchrotron microbeam XAS study
Magmatic gas exsolving during late-stage cooling of shallow magmas has been considered an important facilitator of low-pressure alteration and metal transport. However, the chemical properties of such gas, particularly its metal transport mechanisms and capacity, remain elusive. Trace elements in minerals produced by gas-mediated surface reaction or precipitation from gas capture details of gas composition and reaction pathways. However, interpretation of mineral trace element contents is dependent on understanding crystallographic controls on gas/mineral partitioning. Here, this work investigates the structural accommodation of As, Mn, Ga, Ge, Fe, and Ti in vapor-deposited topaz of vesicular topaz rhyolite from the Thomas Range, Utah, through single-crystal synchrotron microbeam X-ray techniques on picogram quantities of those trace elements. X-ray absorption near edge structure (XANES) data indicates that these elements are incorporated into topaz as As 5+ , Fe 3+ , Mn 3+ , Ti 4+ , Ga 3+ , and Ge 4+ . Extended X-ray absorption fine structure (EXAFS) analysis for these trace elements, compared to EXAFS of structural Al and Si, reveals that As 5+ and Ge 4+ are incorporated directly into the tetrahedral site of the topaz structure, with the octahedral site accommodating Mn 3+ , Fe 3+ , Ga 3+ , and Ti 4+ . For As 5+ and Fe 3+ , the structural impact of substitution extends to at least second neighbors (other elements were only resolvable to first neighbors). Further interpretation of the EXAFS results suggests that the substitution of Ti 4+ results in increased distortion of the octahedral site, while the other trace elements induce more uniform expansion correlating in magnitude to their ionic radius. Comparison of quantified X-ray fluorescence (XRF) data for two topaz crystals from this rhyolite reveals variable trace element concentrations for As 5+ , Fe 3+ , Ga 3+ , and Ti 4+ , reflective of a source gas undersaturated in these trace elements changing in concentration over the period of topaz deposition. The identical Ge 4+ content of the two topaz crystals suggests that Ge 4+ in the gas was buffered by the growth of another Ge 4+ -bearing phase, such as quartz. The very low Mn 3+ content in the topaz crystals does not reflect the abundance of Mn 3+ in the gas (saturation of Mn is evidenced by coexisting bixbyite). Instead, it suggests a strong Jahn-Teller inhibitory effect to the substitution of Mn 3+ for Al 3+ in the distorted octahedral site of topaz. It is proposed that exsolution of an HF-enriched gas from cooling rhyolitic magma led to local scouring of Al, Si, and trace metals from the magma. Once topaz crystals nucleated, self-catalyzed reactions that recycle HF led to continued growth of topaz.
36 MATERIALS SCIENCE↗
Tracing the Evolution of the Cool Gas in CGM and IGM Environments through Mg II Absorption from Redshift z = 0.75 to z = 1.65 Using DESI-Y1 Data
We present a measurement of the mean absorption of cool gas traced by Mg II (λλ2796, 2803) around emission line galaxies (ELGs), spanning spatial scales from 20 kpc to 10 Mpc. The measurement is based on crossmatching the positions ELGs at z = 0.75–1.65 and the metal absorption in the spectra of background quasars with data provided by the Year 1 sample of the Dark Energy Spectroscopic Instrument. The ELGs are divided into two redshift intervals: 0.75 < z < 1.0 and 1.0 < z < 1.65. We find that the composite quasar spectra constructed by stacking the ELG-QSO pairs evolve with redshift, with z > 1 having a systematically higher signal of Mg II absorption. Within 1 Mpc, the covering fraction of the cool gas at z > 1 is higher than that of z < 1. The enhancement becomes less apparent especially if the projected distance r p > 1 Mpc. ELGs with higher stellar mass and star formation rate (SFR) yield higher clustering of Mg ii absorbers at z < 1. For z > 1, the covering fractions with different SFRs show little difference. The higher Mg II absorption at higher redshift supports the observations of higher star formation at cosmic noon. Converting the Mg II absorbers to unsaturated Si II , our estimate indicates that the metal abundance of Si II ranges from 0.7 to 1.2 × 10 −6 from z = 0.9 to 1.3. The growth of low-ionization metal abundance strongly suggests a metal-enriched circumgalactic medium and an increased presence of cool gas in the intergalactic medium toward higher redshifts.
circumgalactic medium↗
Trace Gases, Meteorological Parameters, and GPS data for TRACER-MAP
This dataset includes trace gas, meteorological, and GPS measurements made on Mobile Air Quality Laboratory 2 (MAQL2) for the TRACER-MAP sampling campaign. Trace gas measurements include nitric oxide (NO), nitrogen dioxide (NO2), reactive nitrogen compounds (NOy), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO). All trace gases are in units of ppbv, excluding CO which is in ppmv. The dataset also includes the photolysis rate of NO2 (jNO2), latitude and longitude, wind speed (WS), wind direction (WD), ambient relative humidity (RH), ambient pressure (press), and ambient temperature (temp). Units for trace gases are in parts per billion (ppbv), latitude and longitude are in decimal degrees (dd), jNO2 is in per second (per sec), pressure is in millibar (mbar), relative humidity is in percent, the temperature is in degrees C, wind direction is in degrees, and wind speed is in meters per second. All data is 5 minutes averaged.
54 ENVIRONMENTAL SCIENCES↗
Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning
Quantifying methane emissions is essential for meeting near-term climate goals and is typically carried out using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas production sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings, resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm, with maximum values of 117.5 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios shows overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML-derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher priced trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deployment, and allow for timely and accurate tracking of methane emissions.
03 NATURAL GAS↗
TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - Tracegases
Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)
54 ENVIRONMENTAL SCIENCES↗
TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - APS
Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)
54 ENVIRONMENTAL SCIENCES↗
TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - SP2
Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)
54 ENVIRONMENTAL SCIENCES↗
TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - SPAMS
Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)
54 ENVIRONMENTAL SCIENCES↗
TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - SMPS
Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)
54 ENVIRONMENTAL SCIENCES↗
TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - CAPS-SSA
Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)
54 ENVIRONMENTAL SCIENCES↗
TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - PASS
Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)
54 ENVIRONMENTAL SCIENCES↗
TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - CCN
Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)
54 ENVIRONMENTAL SCIENCES↗
Automated identification of local contamination in remote atmospheric composition time series
Abstract. Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be “tuned” to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).
54 ENVIRONMENTAL SCIENCES↗
Tracing cosmic gas in filaments and halos: Low-redshift insights from the kinematic Sunyaev-Zel’dovich effect
In this work, we leverage cosmic microwave background (CMB) data from the Atacama Cosmology Telescope (ACT) and LSS data from the imaging survey conducted by the Dark Energy Spectroscopic Instrument (DESI) to study the distribution of gas around galaxy groups at low redshift, z ≈ 0.3, via the kinematic Sunyaev-Zel'dovich (kSZ) effect. In particular, we perform velocity-weighted stacking on the photometric galaxies from the Bright Galaxy Survey (BGS) to isolate the monopole and quadrupole of the kSZ signal, orienting the stacked images along 2D filaments identified using the Hessian of the projected gravitational potential. We find a 7.2σ detection in the monopole of the signal (i.e., the gas density profile) and a 4σ detection in the quadrupole (m = 2), constituting the first measurement of the alignment between gas distribution and the cosmic web through the kSZ effect. As it is a linear probe of the local gas density, the kSZ has heightened sensitivity to the warm-hot intergalactic medium (WHIM), which is believed to house the majority of the "missing baryons."Mapping out the gas density at low redshifts, as enabled by our measurements, is crucial for weak lensing surveys, for which the impact of baryons on small scales is a major impediment. We compare the anisotropic signal against two hydrodynamical simulations, TNG300-1 and Illustris, which have very different baryonic feedback prescriptions. We find that the anisotropic signal measured in the data is comparable but slightly larger and more extended compared with the simulations. Further, this suggests that there is excess accretion and feedback taking place through the filaments, hinting at the possible presence of spin-filament alignment of the BGS objects.
79 ASTRONOMY AND ASTROPHYSICS↗
ARM FY2024 Aerosol Operations Plan
The Atmospheric Radiation Measurement user facility (ARM) deploys a suite of aerosol and trace gas (further mentions of aerosols will assume inclusion of trace gases) instrumentation at each of ARM's observatories. ARM currently deploys five Aerosol Observing Systems, one each at: Southern Great Plains (SGP); Eastern North Atlantic (ENA); ARM Mobile Fabilities (AMFI 1, 2, and 3). Aerosol measurements at ARM’s North Slope of Alaska (NSA) observatory have historically been made by the National Oceanic and Atmospheric Administration (NOAA) and provided to ARM through a collaboration. As ARM’s support for aerosol instrumentation increases, it is important for ARM to communicate plans and priorities for aerosol measurements to the community to maximize the benefit and planning around scientific activities and to advance confidence in ARM’s aerosol measurements. ARM will develop a yearly aerosol operations plan for the upcoming fiscal year (October 1-September 30) starting with FY24. This plan will be open and available through the ARM aerosol instrument webpages and will include: Review of the previous activities since the last plan (in this case, 2018); Planned activities and their priorities for the upcoming FY; Calibration timing and efforts; Planned activities for data products and value-added products (VAP).
54 ENVIRONMENTAL SCIENCES↗
ARM FY2025 Aerosol Operations Plan
The U.S. Department of Energy’s Atmospheric Radiation Measurement user facility (ARM) deploys at each of ARM’s observatories a suite of aerosol and trace gas (further mention of aerosols will assume inclusion of trace gases) instrumentation that constitute the Aerosol Observing Systems (AOS; Uin et al. 2019).
54 ENVIRONMENTAL SCIENCES↗