Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Inventory”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Galaxy–Galaxy Strong Lensing Cross Section and the Internal Distribution of Matter in ΛCDM Substructure

Abstract Strong gravitational lensing offers a powerful probe of the detailed distribution of matter in lenses, while magnifying and bringing faint background sources into view. Observed strong lensing by massive galaxy clusters, which are often in complex dynamical states, has also been used to map their dark matter (DM) substructures on smaller scales. Deep high-resolution imaging has revealed the presence of strong lensing events associated with these substructures, namely galaxy-scale sub-halos. However, an inventory of these observed galaxy–galaxy strong lensing (GGSL) events is noted to be discrepant with state-of-the-art ΛCDM simulations. Cluster sub-halos appear to be over-concentrated compared to their simulated counterparts yielding an order-of-magnitude higher value of GGSL. In this paper, we explore the possibility of resolving this observed discrepancy by redistributing the mass within observed cluster sub-halos in ways that are consistent within the ΛCDM paradigm of structure formation. Lensing mass reconstructions from data provide constraints on the mass enclosed within apertures and are agnostic to the detailed mass profile within them. Therefore, as the detailed density profile within cluster sub-halos currently remains unconstrained by data, we are afforded the freedom to redistribute the enclosed mass. We investigate if rearranging the mass to a more centrally concentrated density profile helps alleviate the GGSL discrepancy. We report that refitting cluster sub-halos to the ubiquitous ΛCDM-motivated Navarro–Frenk–White profile, and further modifying them to include significant baryonic components, does not resolve this tension. A resolution to this persisting GGSL discrepancy may require more careful exploration of alternative DM models.

Astronomy & Astrophysics↗
DOE OSTI2025

SAGAbg. III. Environmental Stellar Mass Functions, Self-quenching, and the Stellar-to-halo Mass Relation in the Dwarf Galaxy Regime

Recent efforts have extended our view of the number and properties of satellite galaxies beyond the Local Group firmly down to M ⋆ ∼ 10 6 M ⊙ . A similarly complete view of the field dwarf population has lagged behind. Using the background galaxy sample from the Satellites Around Galactic Analogs (SAGA) survey at z < 0.05, we take inventory of the dwarf population down to M ⋆ ∼ 5 × 10 6 M ⊙ using three metrics: the stellar mass function (SMF) as a function of environment, the stellar-to-halo mass relation (SHMR) of dwarf galaxies inferred via abundance matching, and the quenched fraction of highly isolated dwarfs. We find that the low-mass SMF shape shows minimal environmental dependence, with the field dwarf SMF described by a low-mass power-law index of α 1 = −1.44 ± 0.09 down to M ⋆ ∼ 5 × 10 6 M ⊙ , and that the quenched fraction of isolated dwarfs drops monotonically to f q ∼ 10 −3 at M ⋆ ∼ 10 8.5 M ⊙ . Though slightly steeper than estimates from H I kinematic measures, our inferred SHMR agrees with literature measurements of satellite systems, consistent with minimal environmental dependence of the SHMR in the probed mass range. Finally, although most contemporary cosmological simulations against which we compare accurately predict the SAGAbg-SMF SHMR, we find that big-box cosmological simulations largely overpredict isolated galaxy quenched fractions via a turnaround in f q (M ⋆ ) at 10 8 ≲ M ⋆ /M ⊙ ≲ 10 9 , underscoring the complexities in disentangling the drivers of galaxy formation and the need for systematic multidimensional observations of the dwarf population across environments.

Kado-Fong, Erin [Yale Univ., New Haven, CT (United↗
DOE OSTI2021

Placing High-redshift Quasars in Perspective: A Catalog of Spectroscopic Properties from the Gemini Near Infrared Spectrograph–Distant Quasar Survey

Here, we present spectroscopic measurements for 226 sources from the Gemini Near Infrared Spectrograph–Distant Quasar Survey (GNIRS-DQS). Being the largest uniform, homogeneous survey of its kind, it represents a flux-limited sample ($m_i$ ≲ 19.0 mag, H ≲ 16.5 mag) of Sloan Digital Sky Survey (SDSS) quasars at 1.5 ≲ $\mathcal{z}$ ≲ 3.5 with a monochromatic luminosity ($\lambda {L}_{\lambda }$) at 5100 Å in the range of 10 44 –10 46 erg s –1 . A combination of the GNIRS and SDSS spectra covers principal quasar diagnostic features, chiefly the C IV λ1549, Mg ii λλ2798, 2803, Hβ λ4861, and [O III ] λλ4959, 5007 emission lines, in each source. The spectral inventory will be utilized primarily to develop prescriptions for obtaining more accurate and precise redshifts, black hole masses, and accretion rates for all quasars. Additionally, the measurements will facilitate an understanding of the dependence of rest-frame ultraviolet–optical spectral properties of quasars on redshift, luminosity, and Eddington ratio, and test whether the physical properties of the quasar central engine evolve over cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The R -process Alliance: A Nearly Complete R -process Abundance Template Derived from Ultraviolet Spectroscopy of the R -process-enhanced Metal-poor Star HD 222925 *

We present a nearly complete rapid neutron-capture process (r-process) chemical inventory of the metal-poor ([Fe/H] = -1.46 ± 0.10) r-process-enhanced ([Eu/Fe] = +1.32 ± 0.08) halo star HD 222925. This abundance set is the most complete for any object beyond the solar system, with a total of 63 metals detected and seven with upper limits. It comprises 42 elements from 31 ≤ Z ≤ 90, including elements rarely detected in r-process-enhanced stars, such as Ga, Ge, As, Se, Cd, In, Sn, Sb, Te, W, Re, Os, Ir, Pt, and Au. We derive these abundances from an analysis of 404 absorption lines in ultraviolet spectra collected using the Space Telescope Imaging Spectrograph on the Hubble Space Telescope and previously analyzed optical spectra. A series of appendices discusses the atomic data and quality of fits for these lines. The r-process elements from Ba to Pb, including all elements at the third r-process peak, exhibit remarkable agreement with the solar r-process residuals, with a standard deviation of the differences of only 0.08 dex (17%). In contrast, deviations among the lighter elements from Ga to Te span nearly 1.4 dex, and they show distinct trends from Ga to Se, Nb through Cd, and In through Te. The r-process contribution to Ga, Ge, and As is small, and Se is the lightest element whose production is dominated by the r-process. The lanthanide fraction, log X La = -1.39 ± 0.09, is typical for r-process-enhanced stars and higher than that of the kilonova from the GW170817 neutron-star merger event. We advocate adopting this pattern as an alternative to the solar r-process-element residuals when confronting future theoretical models of heavy-element nucleosynthesis with observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science

Abstract The Vera C. Rubin Observatory is expected to start the Legacy Survey of Space and Time (LSST) in early to mid-2025. This multiband wide-field synoptic survey will transform our view of the solar system, with the discovery and monitoring of over five million small bodies. The final survey strategy chosen for LSST has direct implications on the discoverability and characterization of solar system minor planets and passing interstellar objects. Creating an inventory of the solar system is one of the four main LSST science drivers. The LSST observing cadence is a complex optimization problem that must balance the priorities and needs of all the key LSST science areas. To design the best LSST survey strategy, a series of operation simulations using the Rubin Observatory scheduler have been generated to explore the various options for tuning observing parameters and prioritizations. We explore the impact of the various simulated LSST observing strategies on studying the solar system’s small body reservoirs. We examine what are the best observing scenarios and review what are the important considerations for maximizing LSST solar system science. In general, most of the LSST cadence simulations produce ±5% or less variations in our chosen key metrics, but a subset of the simulations significantly hinder science returns with much larger losses in the discovery and light-curve metrics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗
DOE OSTI2024

Modeling uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI.

greenhouse ga emmission↗
DOE OSTI2019

Model-measurement consistency and limits of bioaerosol abundance over the continental United States

Abstract. Due to low concentrations and chemical complexity, in situ observations of bioaerosol are geographically and temporally sparse, and this limits the accuracy of current emissions inventories. In this study, we apply a new methodology, including corrections for misidentification of mineral dust, to measurements of single particles over four airborne sampling campaigns to derive vertical profiles of bioaerosol over the continental United States. The new methodology is based on single-particle mass spectrometry (SPMS); it can extend historic datasets to include measurements of bioaerosol, it allows comparisons to other techniques, and it generally agrees with a global aerosol model. In the locations sampled, bioaerosols were at least a factor of 10 less abundant than mineral dust. Below 2 km, bioaerosol concentrations were measured between 6×103 and 2×104 m−3. Between 2 and 8 km, bioaerosol concentrations were between 0 and 2×104 m−3, and above 8 km, bioaerosol concentrations were between 0 and 1×103 m−3. Between 30 % and 80 % of single bioaerosol particles detected were internally mixed with dust. A direct comparison of the SPMS methodology with a co-located wideband integrated bioaerosol sensor (WIB) fluorescence sensor on a mountaintop site showed agreement to within a factor of 3 over the common size range.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An evaluation of global organic aerosol schemes using airborne observations

Chemical transport models have historically struggled to accurately simulate the magnitude and variability of observed organic aerosol (OA), with previous studies demonstrating that models significantly underestimate observed concentrations in the troposphere. In this study, we explore two different model OA schemes within the standard GEOS-Chem chemical transport model and evaluate the simulations against a suite of 15 globally distributed airborne campaigns from 2008 to 2017, primarily in the spring and summer seasons. These include the ATom, KORUS-AQ, GoAmazon,FRAPPE, SEAC4RS, SENEX, DC3, CalNex, OP3, EUCAARI, ARCTAS and ARCPAC campaigns and provide broad coverage over a diverse set of atmospheric composition regimes – anthropogenic, biogenic, pyrogenic and remote. The schemes include significant differences in their treatment of the primary and secondary components of OA – a “simple scheme” that models primary OA (POA) as non-volatile and takes a fixed-yield approach to secondary OA (SOA) formation and a “complex scheme” that simulates POA as semi-volatile and uses a more sophisticated volatility basis set approach for non-isoprene SOA, with an explicit aqueous uptake mechanism to model isoprene SOA. Despite these substantial differences, both the simple and complex schemes perform comparably across the aggregate dataset in their ability to capture the observed variability (with an R2 of 0.41 and 0.44, respectively). The simple scheme displays greater skill in minimizing the overall model bias (with a normalized mean bias of 0.04 compared to 0.30 for the complex scheme). Across both schemes, the model skill in reproducing observed OA is superior to previous model evaluations and approaches the fidelity of the sulfate simulation within the GEOS-Chem model. However,there are significant differences in model performance across different chemical source regimes, classified here into seven categories.Higher-resolution nested regional simulations indicate that model resolution is an important factor in capturing variability in highly localized campaigns, while also demonstrating the importance of well-constrained emissions inventories and local meteorology, particularly over Asia. Our analysis suggests that a semi-volatile treatment of POA is superior to anon-volatile treatment. It is also likely that the complex scheme parameterization overestimates biogenic SOA at the global scale. While this study identifies factors within the SOA schemes that likely contribute to OA model bias (such as a strong dependency of the bias in the complex scheme on relative humidity and sulfate concentrations), comparisons with the skill of the sulfate aerosol scheme in GEOS-Chem indicate the importance of other drivers of bias, such as emissions, transport and deposition, that are exogenous to the OA chemical scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Exploration of oxidative chemistry and secondary organic aerosol formation in the Amazon during the wet season: explicit modeling of the Manaus urban plume with GECKO-A

The GoAmazon 2014/5 field campaign took place in Manaus, Brazil, and allowed the investigation of the interaction between background-level biogenic air masses and anthropogenic plumes. We present in this work a box model built to simulate the impact of urban chemistry on biogenic secondary organic aerosol (SOA) formation and composition. An organic chemistry mechanism is generated with the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to simulate the explicit oxidation of biogenic and anthropogenic compounds. A parameterization is also included to account for the reactive uptake of isoprene oxidation products on aqueous particles. The biogenic emissions estimated from existing emission inventories had to be reduced to match measurements. The model is able to reproduce ozone and NO x for clean and polluted situations. The explicit model is able to reproduce background case SOA mass concentrations but does not capture the enhancement observed in the urban plume. The oxidation of biogenic compounds is the major contributor to SOA mass. A volatility basis set (VBS) parameterization applied to the same cases obtains better results than GECKO-A for predicting SOA mass in the box model. The explicit mechanism may be missing SOA-formation processes related to the oxidation of monoterpenes that could be implicitly accounted for in the VBS parameterization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Modeling secondary organic aerosol formation from volatile chemical products

Abstract. Volatile chemical products (VCPs) are commonly used consumer and industrial items that are an important source of anthropogenic emissions. Organic compounds from VCPs evaporate on atmospherically relevant timescales and include many species that are secondary organic aerosol (SOA) precursors. However, the chemistry leading to SOA, particularly that of intermediate-volatility organic compounds (IVOCs), has not been fully represented in regional-scale models such as the Community Multiscale Air Quality (CMAQ) model, which tend to underpredict SOA concentrations in urban areas. Here we develop a model to represent SOA formation from VCP emissions. The model incorporates a new VCP emissions inventory and employs three new classes of emissions: siloxanes, oxygenated IVOCs, and nonoxygenated IVOCs. VCPs are estimated to produce 1.67 µg m−3 of noontime SOA, doubling the current model predictions and reducing the SOA mass concentration bias from −75 % to −58 % when compared to observations in Los Angeles in 2010. While oxygenated and nonoxygenated intermediate-volatility VCP species are emitted in similar quantities, SOA formation is dominated by the nonoxygenated IVOCs. Formaldehyde and SOA show similar relationships to temperature and bias signatures, indicating common sources and/or chemistry. This work suggests that VCPs contribute up to half of anthropogenic SOA in Los Angeles and models must better represent SOA precursors from VCPs to predict the urban enhancement of SOA.

Pennington, Elyse A. (ORCID:0000000317362342)↗
DOE OSTI2023

Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

Abstract. According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h−1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h−1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Implications of reduced-complexity aerosol thermodynamics on organic aerosol mass concentration and composition over North America

Atmospheric organic aerosol (OA) mass concentrations can be affected by water uptake through its impact on the gas–particle partitioning of semi-volatile compounds. Current chemical transport models (CTMs) neglect this process. We have implemented the Binary Activity Thermodynamics model coupled to a volatility basis set partitioning scheme in the GEOS-Chem CTM, providing an efficient reduced-complexity OA model that predicts relative-humidity-dependent mixing and partitioning thermodynamics, while limiting the impact on computational efficiency. We provide a quantitative assessment of this water-sensitive OA treatment, focusing on a subdomain over North America. The updated OA scheme predicts a spatiotemporal mean enhancement in surface-level OA mass concentration of 145 % for January 2019 and 76 % for July 2019 compared to GEOS-Chem's most advanced OA scheme. The temporal mean surface-level OA organic mass concentration can increase by up to ∼590 % for January 2019 and ∼280 % for July 2019, with the greatest enhancements occurring over the ocean. The updated OA scheme also quantifies the OA-associated water content. The simulations show how different OA precursors and related OA surrogates contribute and respond to water uptake, including that due to changes in temperature and relative humidity over the diurnal cycle in selected winter and summer months. These results are independent of future CTM improvements involving updates to chemical reaction schemes and emission inventories. Our water-sensitive OA scheme allows for a better representation of the seasonal and regional variations in OA mass concentration in CTMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Weak influence of anthropogenic emissions on aerosol, cloud, and rain in the wet season of the Amazon rainforest

Anthropogenic emissions have been shown to affect new particle formation, aerosol concentrations, and clouds. Such effects vary with region, environmental conditions, and cloud types. In the wet season of Amazonia, anthropogenic emissions emitted from Manaus, Brazil, can significantly increase the cloud condensation nuclei (CCN) concentrations compared to the background of mainly natural aerosols. However, the regional response of cloud and rain to anthropogenic emissions in Amazonia remains very uncertain. Here, we aim to quantify how aerosol concentration, cloud, and rain respond to changes in anthropogenic emissions through parameterized new particle formation and primary aerosol emission in the Manaus region and to understand the underlying mechanisms. We ran the atmosphere-only configuration of the HadGEM3 climate model with a nested regional domain that covers most of the rainforest region (720 km by 1200 km with 3 km resolution) under scaled regional emissions. The 7 d simulations show that, in the areas that are affected by anthropogenic emissions, when aerosol and precursor gas emissions are doubled from the baseline emission inventories, aerosol number concentrations increase by 13 %. The nucleation rate that involves sulfuric acid and biogenic compounds generally increases with pollution levels. However, nucleation is suppressed very close to the pollution source, resulting in lower nucleation and soluble Aitken mode aerosol number concentrations. We also found that doubling the anthropogenic emission can increase the cloud droplet number concentrations ( N d ) by 9 %, but cloud water and rain mass mixing ratios do not change significantly. Even very strong reductions in aerosol number concentrations by a factor of 4, which is an unrealistic condition, cause only a 4 % increase in rain over the domain. If we assume our simulation has a fine enough grid resolution and an accurate representation of the relevant atmospheric processes, the simulated weak and non-linear response of cloud and rain properties to linearly scaled anthropogenic emissions suggests that the interactions among aerosol, cloud, and precipitation in the Amazonian convective environment are buffered by microphysical processes. It also implies that the convective environment is resilient to the changes in Nd that occur in response to localized anthropogenic aerosol perturbations.

Wang, Xuemei [University of Leeds (United Kingdom)↗
DOE OSTI2023

Airborne flux measurements of ammonia over the southern Great Plains using chemical ionization mass spectrometry

Abstract. Ammonia (NH3) is an abundant trace gas in the atmosphere and an important player in atmospheric chemistry, aerosol formation and the atmosphere–surface exchange of nitrogen. The accurate determination of NH3 emission rates remains a challenge, partly due to the propensity of NH3 to interact with instrument surfaces, leading to high detection limits and slow response times. In this paper, we present a new method for quantifying ambient NH3, using chemical ionization mass spectrometry (CIMS) with deuterated benzene cations as reagents. The setup aimed at limiting sample–surface interactions and achieved a 1σ precision of 10–20 pptv and an immediate 1/e response rate of < 0.4 s, which compares favorably to the existing state of the art. The sensitivity exhibited an inverse humidity dependence, in particular in relatively dry conditions. Background of up to 10 % of the total signal required consideration as well, as it responded on the order of a few minutes. To showcase the method's capabilities, we quantified NH3 mixing ratios from measurements obtained during deployment on a Gulfstream I aircraft during the HI-SCALE (Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems) field campaign in rural Oklahoma during May 2016. Typical mixing ratios were 1–10 parts per billion by volume (ppbv) for the boundary layer and 0.1–1 ppbv in the lower free troposphere. Sharp plumes of up to tens of ppbv of NH3 were encountered as well. We identified two of their sources as a large fertilizer plant and a cattle farm, and our mixing ratio measurements yielded upper bounds of 350 ± 50 and 0.6 kg NH3 h−1 for their respective momentary source rates. The fast response of the CIMS also allowed us to derive vertical NH3 fluxes within the turbulent boundary layer via eddy covariance, for which we chiefly used the continuous wavelet transform technique. As expected for a region dominated by agriculture, we observed predominantly upward fluxes, implying net NH3 emissions from the surface. The corresponding analysis focused on the most suitable flight, which contained two straight-and-level legs at ∼ 300 m above ground. We derived NH3 fluxes between 1 and 11 mol km−2 h−1 for these legs, at an effective spatial resolution of 1–2 km. The analysis demonstrated how flux measurements benefit from suitably arranged flight tracks with sufficiently long straight-and-level legs, and it explores the detrimental effect of measurement discontinuities. Following flux footprint estimations, comparison to the NH3 area emissions inventory provided by the U.S. Environmental Protection Agency indicated overall agreement but also the absence of some sources, for instance the identified cattle farm. Our study concludes that high-precision CIMS measurements are a powerful tool for in situ measurements of ambient NH3 mixing ratios, and even allow for the airborne mapping of the air–surface exchange of NH3.

58 GEOSCIENCES↗
DOE OSTI2022

Airborne flux measurements of ammonia over the Southern Great Plains using chemical ionization mass spectrometry

Ammonia (NH 3 ) is an abundant trace gas in the atmosphere and an important player in atmospheric chemistry, aerosol formation and the atmosphere-surface exchange of nitrogen. It is recognized as a major source of aerosol pollution, and it may limit the formation of cloud nuclei in remote or cold parts of the atmosphere. For soil and plants, NH 3 -mediated nitrogen can act as a harmful pollutant or as a desirable nutrient, mostly in natural and agricultural settings, respectively. Agriculture is also the main source of atmospheric NH 3 via volatilization from fertilizers and manure processing in livestock farming. The accurate determination of NH 3 emission rates remains a challenge, partly due to the propensity of NH 3 to interact with instrument surfaces leading to high detection limits and slow response times. In this paper, we present a new method for quantifying ambient NH 3 , using chemical ionization mass spectrometry (CIMS) with deuterated benzene cations as reagents. The setup aimed at limiting sample-surface interactions and achieved a 1-σ precision of 10–20 pptv and an immediate 1/e response rate < 0.4 s, which compares favorably to the existing state of the art. The sensitivity exhibited an inverse humidity dependence, in particular in relatively dry conditions. Background of up to 10 % of the total signal required consideration as well, as it responded on the order of a few minutes. To showcase the method’s capabilities, we quantified NH3 mixing ratios from measurements obtained during deployment on a Gulfstream I aircraft during the HI-SCALE (Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems) field campaign in rural Oklahoma during May 2016. Typical mixing ratios were 1–10 parts per billion by volume (ppbv) for the boundary layer and 0.1–1 ppbv in the lower free troposphere. Sharp plumes of up to 10s of ppbv of NH3 were encountered as well. We identified two of their sources as a large fertilizer plant and a cattle farm, and our mixing ratio measurements yielded upper bounds of 350 ± 50 and 0.6 kg NH 3 h –1 for their respective momentary source rates. The fast response of the CIMS also allowed us to derive vertical NH 3 fluxes within the turbulent boundary layer via eddy covariance, for which we chiefly used the continuous wavelet transform technique. As expected for a region dominated by agriculture, we observed predominantly upward fluxes, implying net NH 3 emissions from surface. The corresponding analysis focused on the most suitable flight, which contained two straight-and-level legs at ~300 m above ground. We derived NH 3 fluxes between –4 and 18 mol km –2 h –1 for these legs, at an effective spatial resolution of 1–2 km. The analysis demonstrated how flux measurements benefit from suitably arranged flight tracks with sufficiently long straight-and-level legs, and explores the detrimental effect of measurement discontinuities. Following flux footprint estimations, comparison to the NH 3 area emissions inventory provided by the US Environmental Protection Agency indicated overall agreement, but also the absence of some sources, for instance the identified cattle farm. Our study concludes that high-precision CIMS measurements are a powerful tool for in-situ measurements of ambient NH 3 mixing ratios, and even allow for the airborne mapping of the air-surface exchange of NH 3 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Representativeness assessment of the pan-Arctic eddy covariance site network and optimized future enhancements

Abstract. Large changes in the Arctic carbon balance are expected as warming linked to climate change threatens to destabilize ancient permafrost carbon stocks. The eddy covariance (EC) method is an established technique to quantify net losses and gains of carbon between the biosphere and atmosphere at high spatiotemporal resolution. Over the past decades, a growing network of terrestrial EC tower sites has been established across the Arctic, but a comprehensive assessment of the network's representativeness within the heterogeneous Arctic region is still lacking. This creates additional uncertainties when integrating flux data across sites, for example when upscaling fluxes to constrain pan-Arctic carbon budgets and changes therein. This study provides an inventory of Arctic (here > = 60∘ N) EC sites, which has also been made available online (https://cosima.nceas.ucsb.edu/carbon-flux-sites/, last access: 25 January 2022). Our database currently comprises 120 EC sites, but only 83 are listed as active, and just 25 of these active sites remain operational throughout the winter. To map the representativeness of this EC network, we evaluated the similarity between environmental conditions observed at the tower locations and those within the larger Arctic study domain based on 18 bioclimatic and edaphic variables. This allows us to assess a general level of similarity between ecosystem conditions within the domain, while not necessarily reflecting changes in greenhouse gas flux rates directly. We define two metrics based on this representativeness score: one that measures whether a location is represented by an EC tower with similar characteristics (ER1) and a second for which we assess if a minimum level of representation for statistically rigorous extrapolation is met (ER4). We find that while half of the domain is represented by at least one tower, only a third has enough towers in similar locations to allow reliable extrapolation. When we consider methane measurements or year-round (including wintertime) measurements, the values drop to about 1/5 and 1/10 of the domain, respectively. With the majority of sites located in Fennoscandia and Alaska, these regions were assigned the highest level of network representativeness, while large parts of Siberia and patches of Canada were classified as underrepresented. Across the Arctic, mountainous regions were particularly poorly represented by the current EC observation network. We tested three different strategies to identify new site locations or upgrades of existing sites that optimally enhance the representativeness of the current EC network. While 15 new sites can improve the representativeness of the pan-Arctic network by 20 %, upgrading as few as 10 existing sites to capture methane fluxes or remain active during wintertime can improve their respective ER1 network coverage by 28 % to 33 %. This targeted network improvement could be shown to be clearly superior to an unguided selection of new sites, therefore leading to substantial improvements in network coverage based on relatively small investments.

54 ENVIRONMENTAL SCIENCES↗